SnowPro-Core SnowPro Core Certification Exam Questions and Answers

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

https://docs.snowflake.com/en/sql-reference/external-functions.html

Reader accounts in Snowflake are designed to allow users to read data shared with them but do not have the capability to extract data for use outside of Snowflake. They are intended for consuming shared data within the Snowflake environment only.

 One of the key features of Snowflake’s architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

References:

• Snowflake Documentation on Virtual Warehouses
• SnowPro® Core Certification Study Guide

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Data Loading1

 Snowflake supports several semi-structured data formats for loading data. The supported formats include JSON, Avro, and Parquet12. These formats allow for efficient storage and querying of data that does not conform to a traditional relational database schema.

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

References:

• Snowflake Documentation on Secure Data Sharing
• SnowPro™ Core Certification Companion: Hands-on Preparation and Practice

For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to 100 MB. This size range is optimal for Snowpipe’s continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe1

The most efficient way to get files into Snowflake, especially when new files are being written to a cloud storage location at frequent intervals, is to use Snowpipe with auto-ingest. Snowpipe is Snowflake’s continuous data ingestion service that loads data as soon as it becomes available in a cloud storage location. By setting up cloud provider notifications, Snowpipe can be triggered automatically whenever new files are written to the storage location, ensuring that the data is loaded into Snowflake with minimal latency and without the need for manual intervention or scheduling frequent tasks.

References:

• Snowflake Documentation on Snowpipe
• SnowPro® Core Certification Study Guide

 To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Account Usage Schema1

Column-level security is a feature that allows fine-grained control over access to specific columns within a table. This is particularly useful for managing sensitive data and ensuring that only authorized users can view or manipulate certain pieces of information. According to my last update, this feature was available in the Enterprise Edition or higher editions of Snowflake.

References: Based on my internal data as of 2021, column-level security is an advanced feature typically reserved for higher-tiered editions like the Enterprise Edition in data warehousing solutions such as Snowflake.

https://docs.snowflake.com/en/user-guide/intro-editions.html

In Snowflake, Streams are the objects that track Data Manipulation Language (DML) changes made to tables, such as inserts, updates, and deletes. Streams record these changes along with metadata about each change, enabling actions to be taken using the changed data. This process is known as change data capture (CDC)2.

When a cloned table is replicated to a secondary database in Snowflake, the following occurs:

• C. The physical data is replicated: The actual data of the cloned table is physically replicated to the secondary database. This ensures that the secondary database has its own copy of the data, which can be used for read-only purposes or failover scenarios1.
• E. Metadata pointers to cloned tables are replicated: Along with the physical data, the metadata pointers that refer to the cloned tables are also replicated. This metadata includes information about the structure of the table and any associated properties2.

It’s important to note that while the physical data and metadata are replicated, the secondary database is typically read-only and cannot be used for write operations. Additionally, while there may be additional storage costs associated with the secondary account, this is not a direct result of the replication process but rather a consequence of storing additional data.

References:

• SnowPro Core Exam Prep — Answers to Snowflake’s LEVEL UP: Backup and Recovery
• Snowflake SnowPro Core Certification Exam Questions Set 10

 In all Snowflake editions, two key capabilities are universally available:

• B. Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.
• D. Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake’s commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

References:

• Snowflake Documentation on Security Features
• SnowPro® Core Certification Exam Study Guide

A VARIANT column in Snowflake can store semi-structured data types. This includes:

• B. OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.
• D. ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.

The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.

References:

• Snowflake Documentation on Semi-Structured Data Types
• SnowPro® Core Certification Study Guide

The ACCOUNTADMIN role is the most powerful role in Snowflake and should be limited to a select number of users within an organization. It is responsible for account-level configurations and should not be used for day-to-day object creation or management. Granting a custom role to ACCOUNTADMIN could inadvertently give broad access to users with this role, which is not a recommended security practice.

In Snowflake, Cloud Services costs are incurred when the Cloud Services usage exceeds 10% of the compute usage (measured in credits). Therefore, scenarios A and E would result in Cloud Services charges because the Cloud Services usage is more than 10% of the compute credits used.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake’s official documentation on billing and usage1

Snowflake does not require source data files to be no larger than 16MB. In fact, Snowflake recommends that for optimal load performance, data files should be roughly 100-250 MB in size when compressed. However, it is not recommended to load very large files (e.g., 100 GB or larger) due to potential delays and wasted credits if errors occur. Smaller files should be aggregated to minimize processing overhead, and larger files should be split to distribute the load among compute resources in an active warehouse.

References: Preparing your data files | Snowflake Documentation

 Snowflake maintains a result cache that stores the results of every query for 24 hours. If the same query is executed again within this time frame and the data has not changed, Snowflake will retrieve the data from the result cache instead of using a virtual warehouse to recompute the results2.

 Snowpipe is used for continuous, automated data loading into Snowflake. It uses a COPY INTO <title> statement within a pipe object to load data from files as soon as they are available in a stage. Snowpipe does not execute UDFs, stored procedures, or insert statements. References: Snowpipe | Snowflake Documentation

 To monitor data storage for individual tables, the commands and objects that can be used are ‘SHOW STORAGE BY TABLE;’ and the Information Schema view ‘TABLE_STORAGE_METRICS’. These tools provide detailed information about the storage utilization for tables. References: Snowflake Documentation

Snowflake’s architecture is divided into three layers: database storage, query processing, and cloud services. The metadata, which includes information about the structure of the data, the SQL operations performed, and the service-level policies, is stored in the cloud services layer. This layer acts as the brain of the Snowflake environment, managing metadata, query optimization, and transaction coordination.

 The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Access Control Privileges1

 In the Activity section in Snowsight, Snowflake users can explore the performance of executed queries. This includes monitoring queries, viewing details about queries, including performance data, and exploring each step of an executed query in the query profile1.

 The FLATTEN table function in Snowflake is used to convert semi-structured data, such as JSON or XML, into a relational format. It expands nested arrays or objects into multiple rows, making the data suitable for relational querying3.

To execute a series of SQL statements using a task, a user would use a stored procedure that contains multiple SQL statements and invoke this stored procedure from the task. References: Snowflake Documentation2.

 Clustering keys in Snowflake are used to reorganize large tables based on one or more columns. This feature optimizes the arrangement of data within micro-partitions to improve query performance, especially for large tables where efficient data retrieval is crucial. References: [COF-C02] SnowPro Core Certification Exam Study Guide

https://docs.snowflake.com/en/user-guide/tables-clustering-keys.html

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Section 1.3 - SnowPro Core Certification Study Guide1

The Kafka connector stores formatted information in a single column of type VARIANT. This column type is used to store semi-structured data like JSON or Avro, which allows for flexibility in the data structure

The first user assigned to a new Snowflake account, typically with the ACCOUNTADMIN role, should create at least one additional user with the USERADMIN administrative privilege. This role is responsible for creating and managing users and roles within the Snowflake account. References: Access control considerations | Snowflake Documentation

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

• D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.
• E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

References:

• Snowflake Documentation on Clustering Keys & Clustered Tables
• Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION
• Stack Overflow discussion on cluster key selection in Snowflake

 SnowCD (Snowflake Connectivity Diagnostic Tool) is the best tool provided by Snowflake for troubleshooting network connectivity issues. It helps diagnose and resolve issues related to connecting to Snowflake services

https://docs.snowflake.com/en/user-guide/snowcd.html#:~:text=SnowCD%20(i.e.%20Snowflake%20Connectivity%20Diagnostic,their%20network%20connection%20to%20Snowflake .

Efficient pruning in Snowflake is indicated by the number of partitions scanned out of the total available. If a small percentage of partitions are scanned, it suggests that the pruning process is effectively narrowing down the data, which can lead to improved query performance

The Snowpark API allows developers to create User-Defined Functions (UDFs) in various languages, including Python, which is known for its ease of use and wide adoption in data-related tasks. References: Based on general programming and cloud data service knowledge as of 2021.

 The maximum Time Travel retention period for a transient table in Snowflake is 1 day. This is the default and maximum duration for which Snowflake maintains the historical data for transient tables, allowing users to query data as it appeared at any point within the past 24 hours2.

∗∗VeryComprehensiveExplanation=InSnowflake,variablesaredenotedbyadollarsign(). Variables can be used in SQL statements where a literal constant is allowed, and they must be prefixed with a $ sign to distinguish them from bind values and column names.

With the create data EXCHANGE listing privilege, a Snowflake user can modify the properties of listings and submit them for approval or publishing on the Data Exchange. This allows them to manage and share data sets with consumers effectively. References: Based on general data exchange practices in cloud services as of 2021.

 Snowflake’s secure data sharing feature allows sharing of certain database objects with other Snowflake accounts. Among the options provided, external tables and secure UDFs can be shared

When a database is cloned in Snowflake, it does not retain any privileges that were granted on the source object. The clone will need to have privileges reassigned as necessary for users to access it. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Multi-Factor Authentication (MFA) token caching is typically supported for clients that maintain a persistent connection or session with Snowflake, such as the ODBC driver and Python connector, to reduce the need for repeated MFA challenges. References: Based on general security practices in cloud services as of 2021.

 The OBJECT_CONSTRUCT function is used in Snowflake to create a JSON object from relational data, which is useful when unloading semi-structured data. The SINGLE = TRUE parameter is used when unloading data to ensure that the data is exported as a single file, which can be up to 5 GB in size. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake PUT command is used to upload files from a local file system to Snowflake stages, specifically the user stage, table stage, and internal named stage. These stages are where the data files are temporarily stored before being loaded into Snowflake tables

The Cloud Services layer in Snowflake is responsible for various services, including security (like authentication and authorization) and metadata management (like query parsing and optimization). References: Based on general cloud architecture knowledge as of 2021.

Credit charges for Snowflake virtual warehouses are calculated based on the size of the virtual warehouse and the length of time the warehouse is running. The size determines the compute resources available, and charges are incurred for the time these resources are utilized

 To grant a role the privilege to select data from all current and future tables in a schema, two separate commands are needed. The first command grants the SELECT privilege on all existing tables within the schema, and the second command grants the SELECT privilege on all tables that will be created in the future within the same schema. 

Clones in Snowflake have the advantage of being created almost instantly and saving space by not duplicating storage. This is due to Snowflake’s zero-copy cloning feature, which allows for the creation of object clones without the additional storage costs typically associated with data duplication23. Clones are independent of the original table and do not stay in sync with it, nor do they inherently have better query performance. However, they do inherit the time travel history from the original table at the time of cloning

 A schema in Snowflake is a logical grouping of database objects, such as tables and views, that belongs to a single database. Each schema is part of a namespace in Snowflake, which is inferred from the current database and schema in use for the session5

 Clustering in Snowflake refers to the organization of data within micro-partitions, which are contiguous units of storage within Snowflake tables. Clustering keys can be defined to co-locate similar rows in the same micro-partitions, improving scan efficiency and query performance12.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.

The OPERATE privilege is required for a role to resume a suspended warehouse if auto-resume is not enabled. This privilege allows the role to start, stop, suspend, or resume a virtual warehouse3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

 Storing JSON data in a table with a VARIANT data type is optimal for querying because it allows Snowflake to leverage its semi-structured data capabilities. This approach enables efficient storage and querying without the need for flattening the data, which can be performance-intensive1.

Snowflake’s architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management. 

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

Dynamic Data Masking in Snowflake supports tables and views. These objects can have masking policies applied to their columns to dynamically mask data at query time3.

The Query Profile statistics that indicate intermediate results do not fit in memory are the bytes spilled to local storage and bytes spilled to remote storage2.

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The DATA_SHARING_USAGE schema includes views that display information about listings published in the Snowflake Marketplace or a data exchange, which includes DATA_TRANSFER_HISTORY and LISTING_TELEMETRY_DAILY2.

 A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The BUILD_STAGE_FILE_URL function generates a Snowflake-hosted file URL to a staged file using the stage name and relative file path as inputs2.

Snowflake external tables are read-only, which means data manipulation language (DML) operations like INSERT, RENAME, or ALTER cannot be performed on them. However, external tables can be used for query and join operations3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The LOOP type of loop in Snowflake Scripting does not have a built-in termination condition and requires a BREAK statement to stop executing4.

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

The COPY INTO <table> command in Snowflake supports column reordering and column omission as part of its data transformation capabilities during the load process45.

 The TO_JSON function is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [COF-C02] SnowPro Core Certification Exam Study Guide

In Snowflake, permanent tables and external tables can be shared with other accounts using Secure Data Sharing. Temporary tables, transient tables, and UDTFs are not shareable objects

 To view and modify resource monitors, the least privileges needed are MONITOR and MODIFY. These privileges allow a user to monitor credit usage and make changes to resource monitors3.

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

References = [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s Time Travel feature is used for the maintenance of historical data, allowing users to access and restore data that has been changed or deleted within a defined period4.

 The factors that impact storage costs in Snowflake include the account type (Capacity or On Demand) and the cloud region used by the account. These factors determine the rate at which storage is billed, with different regions potentially having different rates3.

 A masking policy in Snowflake consists of a single data type, with one or more conditions, and one or more masking functions. These components define how the data is masked based on the specified conditions3.

The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory. Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.

References:

• Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

QUSTION NO: 582

How do secure views compare to non-secure views in Snowflake?

A. Secure views execute slowly compared to non-secure views.

B. Non-secure views are preferred over secure views when sharing data.

C. Secure views are similar to materialized views in that they are the most performant.

D. There are no performance differences between secure and non-secure views.

Answer: D

Secure views and non-secure views in Snowflake are differentiated primarily by their handling of data access and security rather than performance characteristics. A secure view enforces row-level security and ensures that the view definition is hidden from the users. However, in terms of performance, secure views do not inherently execute slower or faster than non-secure views. The performance of both types of views depends more on other factors such as underlying table design, query complexity, and system workload rather than the security features embedded in the views themselves.

References:

• Snowflake Documentation on Views: This section provides an overview of both secure and non-secure views, clarifying that the main difference lies in security features rather than performance, thus supporting the assertion that there are no inherent performance differences.

QUSTION NO: 583

When using SnowSQL, which configuration options are required when unloading data from a SQL query run on a local machine? {Select TWO).

A. echo

B. quiet

C. output_file

D. output_format

E. force_put_overwrite

Answer: C, D

When unloading data from SnowSQL (Snowflake's command-line client), to a file on a local machine, you need to specify certain configuration options to determine how and where the data should be outputted. The correct configuration options required are:

• C. output_file: This configuration option specifies the file path where the output from the query should be stored. It is essential for directing the results of your SQL query into a local file, rather than just displaying it on the screen.
• D. output_format: This option determines the format of the output file (e.g., CSV, JSON, etc.). It is crucial for ensuring that the data is unloaded in a structured format that meets the requirements of downstream processes or systems.

These options are specified in the SnowSQL configuration file or directly in the SnowSQL command line. The configuration file allows users to set defaults and customize their usage of SnowSQL, including output preferences for unloading data.

References:

• Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation
• Snowflake Documentation: Configuring SnowSQL at Snowflake Documentation

QUSTION NO: 584

How can a Snowflake user post-process the result of SHOW FILE FORMATS?

A. Use the RESULT_SCAN function.

B. Create a CURSOR for the command.

C. Put it in the FROM clause in brackets.

D. Assign the command to RESULTSET.

Answer: A

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes

QUSTION NO: 585

Which file function gives a user or application access to download unstructured data from a Snowflake stage?

A. BUILD_SCOPED_FILE_URL

B. BUILD_STAGE_FILE_URL

C. GET_PRESIGNED_URL

D. GET STAGE LOCATION

Answer: C

The function that provides access to download unstructured data from a Snowflake stage is:

• C. GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

• Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

QUSTION NO: 586

When should a multi-cluster virtual warehouse be used in Snowflake?

A. When queuing is delaying query execution on the warehouse

B. When there is significant disk spilling shown on the Query Profile

C. When dynamic vertical scaling is being used in the warehouse

D. When there are no concurrent queries running on the warehouse

Answer: A

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

• A. When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

• Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

QUSTION NO: 587

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A. select data:BIKEID

B. select data.BIKE.BIKEID

C. select data:BIKE.BIKEID

D. select data:BIKE:BIKEID

Answer: C

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

QUSTION NO: 588

Which Snowflake tool is recommended for data batch processing?

A. SnowCD

B. SnowSQL

C. Snowsight

D. The Snowflake API

Answer: B

For data batch processing in Snowflake, the recommended tool is:

• B. SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

• Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

QUSTION NO: 589

How does the Snowflake search optimization service improve query performance?

A. It improves the performance of range searches.

B. It defines different clustering keys on the same source table.

C. It improves the performance of all queries running against a given table.

D. It improves the performance of equality searches.

Answer: D

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

• D. It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions.

This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

References:

• Snowflake Documentation: Search Optimization Service at Snowflake Documentation

QUSTION NO: 590

What compute resource is used when loading data using Snowpipe?

A. Snowpipe uses virtual warehouses provided by the user.

B. Snowpipe uses an Apache Kafka server for its compute resources.

C. Snowpipe uses compute resources provided by Snowflake.

D. Snowpipe uses cloud platform compute resources provided by the user.

Answer: C

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

• Snowflake Documentation: Understanding Snowpipe

QUSTION NO: 591

What is one of the characteristics of data shares?

A. Data shares support full DML operations.

B. Data shares work by copying data to consumer accounts.

C. Data shares utilize secure views for sharing view objects.

D. Data shares are cloud agnostic and can cross regions by default.

Answer: C

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

• Snowflake Documentation: Understanding Secure Views in Data Sharing

QUSTION NO: 592

Which DDL/DML operation is allowed on an inbound data share?

A. ALTER TA3LE

B. INSERT INTO

C. MERGE

D. SELECT

Answer: D

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

• Snowflake Documentation: Using Data Shares

QUSTION NO: 593

In Snowflake, the use of federated authentication enables which Single Sign-On (SSO) workflow activities? (Select TWO).

A. Authorizing users

B. Initiating user sessions

C. Logging into Snowflake

D. Logging out of Snowflake

E. Performing role authentication

Answer: B C

Federated authentication in Snowflake allows users to use their organizational credentials to log in to Snowflake, leveraging Single Sign-On (SSO). The key activities enabled by this setup include:

• B. Initiating user sessions: Federated authentication streamlines the process of starting a user session in Snowflake by using the existing authentication mechanisms of an organization.
• C. Logging into Snowflake: It simplifies the login process, allowing users to authenticate with their organization's identity provider instead of managing separate credentials for Snowflake.

References:

• Snowflake Documentation: Configuring Federated Authentication

QUSTION NO: 594

A user wants to upload a file to an internal Snowflake stage using a put command.

Which tools and or connectors could be used to execute this command? (Select TWO).

A. SnowCD

B. SnowSQL

C. SQL API

D. Python connector

E. Snowsight worksheets

Answer: B, E

To upload a file to an internal Snowflake stage using a PUT command, you can use:

• B. SnowSQL: SnowSQL, the command-line client for Snowflake, supports the PUT command, allowing users to upload files directly to Snowflake stages from their local file systems.
• E. Snowsight worksheets: Snowsight, the web interface for Snowflake, provides a user-friendly environment for executing SQL commands, including the PUT command, through its interactive worksheets.

References:

• Snowflake Documentation: Loading Data into Snowflake using SnowSQL
• Snowflake Documentation: Using Snowsight

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

Running a virtual warehouse is required when loading data into a table and when unloading data from a table because these operations require compute resources that are provided by the virtual warehouse23.

 Snowflake supports the storage of semi-structured data using the ARRAY and VARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

When the data retention period is changed at the schema level, all child objects that do not have an explicit retention period set will inherit the new retention period from the schema4.

To determine which user has accessed a database object containing sensitive information, a Snowflake administrator can query the ACCESS_HISTORY view in the ACCOUNT_USAGE schema, which provides information about access to database objects3.

In owner’s rights stored procedures, certain commands are restricted to maintain security and integrity. The SHOW and DESCRIBE commands are limited because they can reveal metadata and structure information that may not be intended for all roles. 

The privileges granted to Snowflake's system-defined roles cannot be revoked. System-defined roles, such as SYSADMIN, ACCOUNTADMIN, SECURITYADMIN, and others, come with a set of predefined privileges that are essential for the roles to function correctly within the Snowflake environment. These privileges are intrinsic to the roles and ensure that users assigned these roles can perform the necessary tasks and operations relevant to their responsibilities.

The design of Snowflake's role-based access control (RBAC) model ensures that system-defined roles have a specific set of non-revocable privileges to maintain the security, integrity, and operational efficiency of the Snowflake environment. This approach prevents accidental or intentional modification of privileges that could disrupt the essential functions or compromise the security of the Snowflake account.

References:

• Snowflake Documentation on Access Control: Understanding Role-Based Access Control (RBAC)

The purpose of the Snowflake SPLIT_TO_TABLE function is to split a string based on a specified delimiter and flatten the results into rows. This table function is useful for transforming a delimited string into a set of rows that can be further processed or queried5.

Based on Snowflake recommendations, when creating a hierarchy of custom roles, the top-most custom role should ideally be granted to the ACCOUNTADMIN role. This recommendation stems from the best practices for implementing a least privilege access control model, ensuring that only the necessary permissions are granted at each level of the role hierarchy. The ACCOUNTADMIN role has the highest level of privileges in Snowflake, including the ability to manage all aspects of the Snowflake account. By assigning the top-most custom role to ACCOUNTADMIN, you ensure that the administration of role hierarchies and the assignment of roles remain under the control of users with the highest level of oversight and responsibility within the Snowflake environment.

References:

• Snowflake Documentation on Access Control: Managing Access Control

• Incorrect Commands: The options referencing "RESUME", "SUSPEND", and "ADD" are not valid commands in the context of secondary roles.

References:

• Snowflake Documentation (USE SECONDARY ROLES): https://docs.snowflake.com/en/sql-reference/sql/use-secondary-roles.html

When a virtual warehouse in Snowflake is suspended, the cache is dropped and is no longer available upon restart. This means that all cached data, including results and temporary data, are cleared from memory. The purpose of this behavior is to conserve resources while the warehouse is not active. Upon restarting the warehouse, it will need to reload any data required for queries from storage, which may result in a slower initial performance until the cache is repopulated. This is a critical consideration for managing performance and cost in Snowflake.

• Managed Access Schemas: These are a special type of schema designed for fine-grained access control in Snowflake.
• Roles with Grant Authority:
• Important Note: The ORGADMIN role focuses on organization-level management, not object access control.

In Snowsight, Snowflake's web interface, user preferences can be customized to enhance the user experience. Among these preferences, users can set a default database and default schema. These settings streamline the user experience by automatically selecting the specified database and schema when the user initiates a new session or query, reducing the need to manually specify these parameters for each operation. This feature is particularly useful for users who frequently work within a specific database or schema context.References: Snowflake Documentation on Snowsight User Preferences

A pre-signed URL provides a way to access objects stored in a stage without requiring authentication to Snowflake. This URL contains all the necessary information for access control, including an expiration time, and it’s signed by the service's credentials. This mechanism allows users or applications to access specific files stored in a cloud storage location (stage) directly, bypassing the need to authenticate into Snowflake, ensuring secure and temporary access to the data.References: Snowflake Documentation on Stages and Data Loading

In Snowflake, a Temporary table is a type of table that is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is automatically dropped at the end of the session in which it was created. Temporary tables are designed for transient data processing needs, where data is needed for the duration of a specific task or session but not beyond. Since they are automatically cleaned up at the end of the session, they help manage storage usage efficiently and ensure that sensitive data is not inadvertently persisted.

References:

• Snowflake Documentation on Temporary Tables: Temporary Tables

Snowsight, Snowflake's user interface for exploring, analyzing, and visualizing data, supports a variety of chart types for creating dashboards and visualizations. One of the supported chart types in Snowsight is the Area Chart (A). Area charts are useful for representing quantities over time and can be used to highlight volume change and rate of change, as well as to compare multiple quantities.

While Snowsight supports many types of visualizations to help users analyze their data effectively, including line charts, bar charts, and scatter plots, it's important to select the specific reference documentation or release notes for the most current list of supported chart types, as Snowflake continues to enhance and update Snowsight's capabilities.

As of the last update, Box plots (B), Heat grids (C), and Pie charts (D) are types of visualizations that may be supported in various analytics and visualization tools, but for the specific context of Snowsight's currently confirmed features, Area charts are a verified option for users to visualize their data.

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on a source table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

References:

• Snowflake Documentation on Streams: Using Streams
• Snowflake Documentation on Data Retention: Understanding Data Retention

When deciding to use a secure view, several considerations come into play, especially concerning security and performance:

A. No details of the query execution plan will be available in the query profiler: Secure views are designed to prevent the exposure of the underlying data and the view definition to unauthorized users. Because of this, the detailed execution plans for queries against secure views are not available in the query profiler. This is intended to protect sensitive data from being inferred through the execution plan.

C. Secure views do not take advantage of the same internal optimizations as standard views: Secure views, by their nature, limit some of the optimizations that can be applied compared to standard views. This is because they enforce row-level security and mask data, which can introduce additional processing overhead and limit the optimizer's ability to apply certain efficiencies that are available to standard views.

B. Once created, there is no way to determine if a view is secure or not is incorrect because metadata about whether a view is secure can be retrieved from the INFORMATION_SCHEMA views or by using the SHOW VIEWS command.

D. It is not possible to create secure materialized views is incorrect because the limitation is not on the security of the view but on the fact that Snowflake currently does not support materialized views with the same dynamic data masking and row-level security features as secure views.

E. The view definition of a secure view is still visible to users by way of the information schema is incorrect because secure views specifically hide the view definition from users who do not have the privilege to view it, ensuring that sensitive information in the definition is not exposed.

The Snowflake feature that helps troubleshoot issues in SQL query expressions and commonly identify performance bottlenecks is the Query Profile. The Query Profile provides a detailed breakdown of a query's execution plan, including each operation's time and resources consumed. It visualizes the steps involved in the query execution, highlighting areas that may be causing inefficiencies, such as full table scans, large joins, or operations that could benefit from optimization.

By examining the Query Profile, developers and database administrators can identify and troubleshoot performance issues, optimize query structures, and make informed decisions about potential schema or indexing changes to improve performance.

References:

• Snowflake Documentation on Query Profile: Using the Query Profile

In Snowflake, a user within a reader account primarily has read-only access to the shared data. Reader accounts are created to enable third parties or separate business units to access and query data shared with them without allowing them to modify the underlying data. This means a user with a reader account can perform operations like querying shared data to analyze or report on it but cannot load new data, update existing data, or create new shares. This setup is crucial for maintaining data governance and security while enabling data sharing and collaboration.References: Snowflake Documentation on Reader Accounts

The ENABLE_ACCOUNT_DATABASE_REPLICATION parameter is a critical setting in Snowflake that allows or restricts the replication of databases across Snowflake accounts. Given the significant impact of this parameter on data management and security, only the ACCOUNTADMIN role has the minimum required privileges to set or modify it. This ensures that only users with the highest level of access and responsibility within the Snowflake environment can control database replication settings, maintaining strict governance and security standards.References: Snowflake Documentation on Database Replication

When attempting to load large files, such as a 125 GB file, into Snowflake using SnowSQL, the process might encounter limitations related to the maximum execution time for queries or data loading operations. If the loading process exceeds this time limit (typically around 24 hours), it could be aborted without committing any part of the file to the database. This behavior is designed to prevent indefinite resource consumption and to maintain system stability, emphasizing the need for optimizing data load operations, possibly through file segmentation or parallel loading strategies.References: Snowflake Documentation on Data Loading Considerations

The privilege required on a virtual warehouse to abort any existing executing queries is OPERATE. The OPERATE privilege on a virtual warehouse allows a user to perform operational tasks on the warehouse, including starting, stopping, and restarting the warehouse, as well as aborting running queries. This level of control is essential for managing resource utilization and ensuring that the virtual warehouse operates efficiently.

References:

• Snowflake Documentation on Access Control: Access Control Privileges

In Snowflake, network policies define the allowed IP address ranges from which users can connect to Snowflake, enhancing security by restricting access based on network location. The creation and management of network policies require sufficient privileges. Specifically, a user with the SECURITYADMIN role or any role with higher privileges, such as ACCOUNTADMIN, can create network policies. Additionally, a custom role can be granted the CREATE NETWORK POLICY privilege, enabling users assigned to that role to also create network policies. This approach allows for flexible and secure management of network access to Snowflake.References: Snowflake Documentation on Network Policies

When sharing data in Snowflake, the provider needs to grant the following privileges along with a share:

• A. USAGE on the specific tables in the database: This privilege allows the consumers of the share to access the specific tables included in the share.
• D. USAGE on the database and the schema containing the tables to share: This privilege is necessary for the consumers to access the database and schema levels, enabling them to access the tables within those schemas.

These privileges are crucial for setting up secure and controlled access to the shared data, ensuring that only authorized users can access the specified resources.

Reference to Snowflake documentation on sharing data and managing access:

• Data Sharing Overview
• Privileges Required for Sharing Data

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, including queries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

• Role of Access History: Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.
• How Access History Supports Compliance Auditing:

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References: Apache Parquet Documentation

The property mins_to_bypass_network_policy is set at the account level in Snowflake. This setting allows administrators to specify a time frame during which users can bypass network policies that have been set on their account. It is particularly useful in scenarios where temporary access needs to be granted from IPs not covered by the existing network policies. By adjusting this property at the account level, Snowflake administrators can manage and enforce network access controls efficiently across the entire account.

References:

• Snowflake Documentation on Network Policies: Network Policies

Snowflake streams are objects that enable users to track changes (inserts, updates, and deletes) to the data in tables, facilitating real-time data processing and integration workflows. Streams can be created on standard tables, capturing DML changes made to these tables so that downstream processes can consume the changes incrementally. This feature supports efficient data ETL, replication, and real-time analytics by providing a mechanism to process only the data that has changed. Note: The correct options should likely include a distinction between "Standard tables" and another object type such as "External tables" rather than repeating "Standard tables" twice.References: Snowflake Documentation on Streams

In Snowflake, resizing a virtual warehouse that is currently suspended does not immediately provision the new compute resources. Instead, the change in size is recorded, and the additional compute resources are provisioned when the warehouse is resumed. This means that the action of resizing a suspended warehouse does not cause it to resume operation automatically. The warehouse remains suspended until an explicit command to resume it is issued, or until it automatically resumes upon the next query execution that requires it.

This behavior allows for efficient management of compute resources, ensuring that credits are not consumed by a warehouse that is not in use, even if its size is adjusted while it is suspended.

The type of Snowflake table that persists until it is explicitly dropped, is available for all users with relevant privileges across sessions, and does not have a Fail-safe period, is a Transient table. Transient tables are designed to provide temporary storage similar to permanent tables but with some reduced storage costs and without the Fail-safe feature, which provides additional data protection for a period beyond the retention time. Transient tables are useful in scenarios where data needs to be temporarily stored for longer than a session but does not require the robust durability guarantees of permanent tables.

The Activity area of Snowsight, Snowflake's web interface, allows users to perform several important tasks related to query management and performance analysis. Among the options provided, the correct ones are:

• B. Explore each step of an executed query: Snowsight provides detailed insights into query execution, including the ability to explore the execution plan of a query. This helps users understand how a query was processed, identify performance bottlenecks, and optimize query performance.
• C. Monitor queries executed by users in an account: The Activity area enables users to monitor the queries that have been executed by users within the Snowflake account. This includes viewing the history of queries, their execution times, resources consumed, and other relevant metrics.

These features are crucial for effective query performance tuning and ensuring efficient use of Snowflake's resources.

References:

• Snowflake Documentation on Snowsight: Using Snowsight

To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, the PUT command is used. The PUT command takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.

Syntax Example:

PUT file:// @;

This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using the COPY INTO command.

A Reader account in Snowflake can be used to share data with a consumer who does not have a Snowflake account. Reader accounts are a type of shared account provided by data providers to external data consumers, allowing them to access and query shared data using Snowflake's web interface without needing their own Snowflake account.

References:

• Snowflake Documentation: Reader Accounts

In the execution time summary of a Query Profile in Snowflake, the categories included provide insights into various aspects of query execution. "Pruning" refers to the process by which Snowflake reduces the amount of data scanned by eliminating partitions of data that are not relevant to the query, thus improving performance. "Initialization" represents the time taken for query planning and setup before actual execution begins. These metrics are crucial for understanding and optimizing query performance.

Snowsight, Snowflake’s user interface for executing and analyzing queries, supports various types of visualizations to help users understand their data better. Among the supported types, area charts and bar charts are two common options. Area charts are useful for representing quantities through the use of filled areas on the graph, often useful for showing volume changes over time. Bar charts, on the other hand, are versatile for comparing different groups or categories of data. Both chart types are integral to data analysis, enabling users to visualize trends, patterns, and differences in their data effectively.References: Snowflake Documentation on Snowsight Visualizations

In the Snowflake Data Exchange, the ability to get or request data from listings is generally controlled by specific roles and privileges:

• A. Users with ACCOUNTADMIN role: This role typically has the highest level of access within a Snowflake account, including the ability to manage and access all features and functions. Users with this role can access data listings within the Data Exchange.
• D. Users with import share privilege: This specific privilege is necessary for users who need to import shared data from the Data Exchange. This privilege allows them to request and access data listings explicitly shared with them.

Temporary and transient tables in Snowflake share several characteristics, notably, neither table type has a Fail-safe period. Fail-safe is a feature that provides additional data protection beyond the Time Travel period. However, this feature does not apply to temporary or transient tables. Additionally, for both types of tables, the data retention period effectively ends when the tables are dropped. This means that once these tables are deleted, their data is not recoverable, distinguishing them from permanent tables, which benefit from Snowflake's Time Travel and Fail-safe features.References: Snowflake Documentation on Table Types

In Snowflake, overloading refers to the creation of multiple User-Defined Functions (UDFs) with the same name but differing in the number or types of their arguments. This feature allows for more flexible function usage, as Snowflake can differentiate between functions based on the context of their invocation, such as the types or the number of arguments passed. Overloading helps to create more adaptable and readable code, as the same function name can be used for similar operations on different types of data.

References:

• Snowflake Documentation: User-Defined Functions

When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake's data warehousing capabilities.

References:

• Snowflake Documentation: Data Storage and Organization

• Object Name Resolution: When unqualified object names (e.g., table name without schema) are used in a UDF, Snowflake follows a specific hierarchy to resolve them. Here's the order:
• Note: The SEARCH_PATH parameter influences object resolution for queries, not within UDFs.

References:

• Snowflake Documentation (Object Naming Resolution): https://docs.snowflake.com/en/sql-reference/name-resolution.html

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

• Snowflake Documentation: Query Performance Optimization

Characteristics of reader accounts in Snowflake include:

• A. Reader account users cannot add new data to the account: Reader accounts are intended for data consumption only. Users of these accounts can query and analyze the data shared with them but cannot upload or add new data to the account.
• E. Reader accounts enable data consumers to access and query data shared by the provider: One of the primary purposes of reader accounts is to allow data consumers to access and perform queries on the data shared by another Snowflake account, facilitating secure and controlled data sharing.

References:

• Snowflake Documentation: Reader Accounts

The replication feature in Snowflake provides several benefits, with disaster recovery and database failover and fallback being two of the primary advantages. Replication allows for the continuous copying of data from one Snowflake account to another, ensuring that a secondary copy of the data is available in case of outages or disasters. This capability supports disaster recovery strategies by allowing operations to quickly switch to the replicated data in a different account or region. Additionally, it facilitates database failover and fallback procedures, ensuring business continuity and minimizing downtime.

References:

• Snowflake Documentation: Data Replication

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

• B. Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.
• C. Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

References:

• Snowflake Documentation: Transient Tables

A Pre-signed URL provides access to files stored in Snowflake without requiring authorization at the time of access. This feature allows users to generate a URL with a limited validity period that grants temporary access to a file in a secure manner. It's particularly useful for sharing data with external parties or applications without the need for them to authenticate directly with Snowflake.

References:

• Snowflake Documentation: Using Pre-signed URLs

A cached MFA token is valid for up to four hours. https://docs.snowflake.com/en/user-guide/security-mfa#using-mfa-token-caching-to-minimize-the-number-of-prompts-during-authentication-optional

The OBJECT_CONSTRUCT function in Snowflake is used to convert rows in a relational table into a single VARIANT column that represents each row as a JSON object. This function dynamically creates a JSON object from a list of key-value pairs, where each key is a column name and each value is the corresponding column value for a row. This is particularly useful for aggregating and transforming structured data into semi-structured JSON format for further processing or analysis.

References:

• Snowflake Documentation: Semi-structured Data Functions

When a Snowflake user increases the size of a suspended virtual warehouse, the changes to compute resources are queued but do not take immediate effect. The provisioning of additional compute resources occurs only when the warehouse is resumed. This ensures that resources are allocated efficiently, aligning with Snowflake's commitment to cost-effective and on-demand scalability.

References:

• Snowflake Documentation: Virtual Warehouses

When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.

References:

• Snowflake Documentation: Data Unloading Considerations

The command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

• Snowflake Documentation: SHOW FILE FORMATS

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated and isolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

References:

• Snowflake Documentation: Snowflake Editions

A Snowflake user would decide to use a materialized view instead of a regular view primarily when the base tables do not change frequently. Materialized views store the result of the view query and update it as the underlying data changes, making them ideal for situations where the data is relatively static and query performance is critical. By precomputing and storing the query results, materialized views can significantly reduce query execution times for complex aggregations, joins, and calculations.

References:

• Snowflake Documentation: Materialized Views

To obtain the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view is the recommended option. This view provides comprehensive metrics on storage usage, including data size, time travel size, fail-safe size, and other relevant storage metrics for each table. This level of detail is invaluable for monitoring, managing, and optimizing storage costs and performance.

References:

• Snowflake Documentation: Information Schema

To unload data from a table into one or more files in a named stage, the COPY INTO <location> command should be used. This command exports the result of a query, such as selecting all rows from a table, into files stored in the specified stage. The COPY INTO command is versatile, supporting various file formats and compression options for efficient data unloading.

References:

• Snowflake Documentation: COPY INTO Location

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

• Snowflake Documentation: Snowflake Architecture

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

• Snowflake Documentation: Data Sharing

In Snowflake, the maximum number of clusters that can be provisioned within a multi-cluster virtual warehouse is 10. This allows for significant scalability and performance management by enabling Snowflake to handle varying levels of query load by adjusting the number of active clusters within the warehouse.References: Snowflake documentation on virtual warehouses, particularly the scalability options available in multi-cluster configurations.

In Snowflake, semi-structured data is optimally stored using specific data types that are designed to handle the flexibility and complexity of such data. The VARIANT data type can store structured and semi-structured data types, including JSON, Avro, ORC, Parquet, or XML, in a single column. The ARRAY data type, on the other hand, is suitable for storing ordered sequences of elements, which can be particularly useful for semi-structured data types like JSON arrays. These data types provide the necessary flexibility to store and query semi-structured data efficiently in Snowflake.

References:

• Snowflake Documentation: Semi-structured Data Types

The default value for auto-suspending a Virtual Warehouse in the Snowflake Web Interface (UI) is 10 minutes. This setting helps manage compute costs by automatically suspending warehouses that are not in use, ensuring that compute resources are efficiently allocated and not wasted on idle warehouses.

References:

• Snowflake Documentation: Virtual Warehouses

When dealing with semi-structured data in Snowflake, and the specific types of operations to be performed on the data are not yet determined, Snowflake recommends using the VARIANT data type. The VARIANT type is highly flexible and capable of storing data in multiple formats, including JSON, AVRO, BSON, and more, within a single column. This flexibility allows users to perform various operations on the data, including querying and manipulation of nested data structures without predefined schemas.

References:

• Snowflake Documentation: Semi-structured Data Types

In Snowflake, stored procedures and User-Defined Functions (UDFs) have different invocation patterns within SQL:

• Option B is correct: A single executable statement can call only one stored procedure due to the procedural and potentially transactional nature of stored procedures. In contrast, a single SQL statement can call multiple UDFs because UDFs are designed to operate more like functions in traditional programming, where they return a value and can be embedded within SQL queries.References: Snowflake documentation comparing the operational differences between stored procedures and UDFs.

In the Query Profile of Snowflake, the TableScan operator represents the access to a single table. This operator indicates that the query execution involved reading data from a table stored in Snowflake. TableScan is a fundamental operation in query execution plans, showing how the database engine retrieves data directly from tables as part of processing a query.

References:

• Snowflake Documentation: Understanding the Query Profile

To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.

References:

• Snowflake Documentation: PARSE_JSON

Network policies in Snowflake are defined as a set of rules that manage the network-level access to Snowflake accounts. These rules specify which IP addresses or IP ranges are permitted to connect to Snowflake, enhancing the security of Snowflake accounts by preventing unauthorized access. Network policies are an essential aspect of Snowflake's security model, allowing administrators to enforce access controls based on network locations.

References:

• Snowflake Documentation: Network Policies

• Purpose: The BUILD_STAGE_FILE_URL function generates a temporary, pre-signed URL that allows you to access a file within a Snowflake stage (internal or external). This URL requires authentication to use.
• Key Points:

• Snowflake Documentation (BUILD_STAGE_FILE_URL): https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html

The TABLE_STORAGE_METRICS view can be used to determine if a table has frequent row updates or deletes. This view provides detailed metrics on the storage utilization of tables within Snowflake, including metrics that reflect the impact of DML operations such as updates and deletes on table storage. For example, metrics related to the number of active and deleted rows can help identify tables that experience high levels of row modifications, indicating frequent updates or deletions.

References:

• Snowflake Documentation: TABLE_STORAGE_METRICS View

By default, the standard retention period for Time Travel in Snowflake is 1 day across all Snowflake accounts. Time Travel enables users to access historical data within this retention window, allowing for point-in-time data analysis and recovery. This feature is a significant aspect of Snowflake's data management capabilities, offering flexibility in handling data changes and accidental deletions.

References:

• Snowflake Documentation: Using Time Travel

 Snowpipe retains load history for 14 days. This allows users to view and audit the data that has been loaded into Snowflake using Snowpipe within this time frame3.

 In Snowflake, a task allows a user to execute a single SQL statement/command using a predefined schedule (B). Tasks are used to automate the execution of SQL statements at scheduled intervals.

Data masking policies in Snowflake allow for the obfuscation of specific data within a field, enabling some users to see the full data while limiting others. This feature is particularly useful for handling PII, ensuring that sensitive information is only visible to authorized users1.

The Enterprise edition of Snowflake allows for a dedicated metadata store, providing additional features designed for large-scale enterprises

The SQL statement SHOW GRANTS TO ROLE MY_ROLE is used to determine which users and roles have access to a role called MY_ROLE. This statement lists all the privileges granted to the role, including which roles and users can assume MY_ROLE. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a Snowflake role hierarchy, the top-level role is ACCOUNTADMIN. This role has the highest level of privileges and is capable of performing all administrative functions within the Snowflake account

 Users are responsible for data storage costs in Snowflake until the data expires from the Fail-safe period. Fail-safe is the final stage in the data lifecycle, following Time Travel, and provides additional protection against accidental data loss. Once data exits the Fail-safe state, users are no longer billed for its storage

Snowflake virtual warehouses support a configuration file and command line options in SnowSQL to specify a default warehouse, which is characteristic C. Additionally, the size of a virtual warehouse can be changed at any time, which is characteristic E. These features provide flexibility and ease of use in managing compute resources2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

 Protegrity is listed as a data tokenization integration partner for Snowflake. This partnership allows Snowflake users to utilize Protegrity’s tokenization solutions within the Snowflake environment3.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 For a temporary table, the maximum total Continuous Data Protection (CDP) charges incurred are for the duration of the session in which the table was created, which does not exceed 24 hours2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation2

The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.

 To ensure that additional multi-clusters are resumed with no delay, a virtual warehouse should be configured to a size larger than generally required. This configuration allows for immediate availability of additional resources when needed, without waiting for new clusters to start up

The error “Max LOB size (16777216) exceeded” indicates that the size of the parsed column exceeds the maximum size allowed for a single column value in Snowflake, which is 16 MB. To resolve this issue, the file should be split into multiple smaller files that are within the recommended size range of 100 MB to 250 MB. This will ensure that each JSON document within the files is smaller than the maximum LOB size allowed. Compressing the file, using a larger-sized warehouse, or setting STRIP_OUTER_ARRAY=TRUE will not resolve the issue of the column size exceeding the maximum allowed. References: COPY INTO Error during Structured Data Load: “Max LOB size (16777216) exceeded…”

The Snowflake Enterprise edition includes database replication and failover for business continuity and disaster recovery, as well as extended time travel capabilities for longer data retention periods1.