Associate-Data-Practitioner Google Cloud Associate Data Practitioner (ADP Exam) Questions and Answers

UsingCloud Composerto design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.

Creating amaterialized view in BigQuerywith theSUM()function and theDATE_SUB()function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using theDATE_SUB()function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

UsingDataflowto create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.

Dataform workflowsare the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let’s evaluate:

Option A: Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.

Option B: Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.

Option C: Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.

Setting thetable partition expiration periodto one year using theALTER TABLEstatement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.

Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery" (https://cloud.google.com/bigquery/docs/partitioned-tables#expiration ):"Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."

UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.

Configuring aCloud Storage lifecycle ruleto automatically delete objects older than seven days is the best solution because:

Built-in feature: Cloud Storage lifecycle rules are specifically designed to manage object lifecycles, such as automatically deleting or transitioning objects based on age.

No additional setup: It requires no external services or custom code, reducing complexity and maintenance.

Cost-effective: It directly achieves the goal of deleting files after seven days without incurring additional compute costs.

To calculate the weekly moving average of sales by location:

The query must group bystore_id(partitioning the calculation by each store).

TheORDER BY dateensures the sales are evaluated chronologically.

TheROWS BETWEEN 6 PRECEDING AND CURRENT ROWspecifies a rolling window of 7 rows (1 week if each row represents daily data).

TheAVG(total_sales)computes the average sales over the defined rolling window.

Chosen querymeets these requirements:

PARTITION BY store_idgroups the calculation by each store.

ORDER BY dateorders the rows correctly for the rolling average.

ROWS BETWEEN 6 PRECEDING AND CURRENT ROWensures the 7-day moving average.

Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts ):"Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id."

Comprehensive and Detailed In-Depth Explanation:

Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let’s analyze:

Option A: BigQuery ML’s time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.

Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML’s in-place processing.

Option C: Linear regression predicts continuous values but doesn’t handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

Extract from Google Documentation: From "Row-Level Security in BigQuery" (https://cloud.google.com/bigquery/docs/row-level-security ):"Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."

To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to useDataflowandBigQuery.Dataflowis a fully managed service for real-time data processing and transformation, whileBigQueryis a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:

Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.

Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.

Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.

Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

roles/bigquery.jobUser:

This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.

From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."

roles/bigquery.dataOwner:

This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.

From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."

Why other options are incorrect:

B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:

roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.

C. roles/bigquery.admin:

roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.

D. roles/bigquery.user and roles/bigquery.filteredDataViewer:

roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.

Principle of Least Privilege:

The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.

By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.

Google Cloud Documentation References:

BigQuery IAM roles:https://cloud.google.com/bigquery/docs/access-control-basic-roles

IAM best practices:https://cloud.google.com/iam/docs/best-practices-for-using-iam

Assigning each analyst to a separate project associated with their department and creating asingle reservation for each departmentusingBigQuery editionsallows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.

UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

UsingCloud Composerto create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.

Extract from Google Documentation: From "Cloud Composer Overview" (https://cloud.google.com/composer/docs ):"Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery."

Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.

Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

Creating amaterialized viewwith the limited data in a new dataset and granting the data analyst theBigQuery Data Viewerrole on the dataset and theBigQuery Job Userrole in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

UsingDataproc workflow templatesis a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.

Comprehensive and Detailed In-Depth Explanation:

A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.

Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging—more effort than necessary for a one-time task.

Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting—far too complex for a simple load.

Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.table file.csv). It’s pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.