DP-600 Implementing Analytics Solutions Using Microsoft Fabric Questions and Answers

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.

The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user ' s credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

Comprehensive Detailed Explanation

When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:

1. Data Engineers

Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.

The appropriate permission is Build Reports on the default dataset , because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.

2. Data Analysts

Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.

The correct permission is Read All Apache Spark , as this grants them the ability to query and analyze Spark-based data directly without elevated permissions that are unnecessary for their role.

3. Data Scientists

Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.

The correct permission is Read All SQL analytics endpoint data , since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.

Summary of Selections:

DataEngineers → Build Reports on the default dataset

DataAnalysts → Read All Apache Spark

DataScientists → Read All SQL analytics endpoint data

References (Microsoft Fabric – DP-600 exam scope):

Workspace roles and permissions in Microsoft Fabric

SQL analytics endpoint in Fabric

Apache Spark in Microsoft Fabric

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft ' s documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of " what has happened " by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.

User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.

The charts in the Fabric notebook are summarizing customer churn data (retained vs lost customers) across dimensions such as geography, products purchased, age, and tenure. This is describing " what has happened " in the data.

Descriptive analytics summarizes and visualizes historical data.

Diagnostic analytics explains " why " events occurred.

Predictive analytics forecasts future outcomes.

Prescriptive analytics recommends actions.

Since only distributions are being shown, this is descriptive analytics.

Comprehensive Detailed Explanation

We have a table with:

CustomerID → unique ID

CustomerName → customer’s name

VersionDate → timestamp representing version history

The requirement:

Keep only the latest record per customer .

Ensure no new technical columns (like max version date) remain in the semantic model.

Step 1: Identify latest version per customer

We need to determine the latest VersionDate for each CustomerID .

Use Group By CustomerID and calculate Max of VersionDate .

This creates a mapping of CustomerID → latest VersionDate.

Step 2: Filter to keep only latest records

Join or filter original data so that only rows with VersionDate = Max(VersionDate) are retained.

This ensures that only the most recent record is kept per customer.

Step 3: Remove the helper column

The column holding Max VersionDate is only needed for filtering.

It must be removed because the requirement is that no new columns are loaded to the semantic model.

Step 4: Remove duplicates

As a final safeguard, remove duplicates by CustomerID , ensuring that each customer appears only once.

Final Ordered Actions

Group by CustomerID and calculate the max version date per customer ID.

Filter the query where the version date value equals the max version date value.

Remove the max version date column.

Remove duplicates based on CustomerID.

Comprehensive Detailed Explanation

We are tasked with writing a T-SQL query to denormalize the tables Employee and Contract in a Fabric warehouse.

Requirements Breakdown

Include attributes from matching rows in the Contract table

This means we must join Employee and Contract on EmployeeID .

Ensure that all rows from the Employee table are preserved

This requires a LEFT OUTER JOIN from Employee to Contract. If an employee has no contract, we still want the employee record included.

Return the total number of employees per contract type for all contract types that have more than two employees

This means we need to:

Group by ContractType

Count distinct EmployeeID

Filter groups where that count > 2.

To filter aggregated results, we use HAVING , not WHERE.

Completed Query

WITH result AS (

SELECT

e.EmployeeID,

e.EmployeeName,

e.EmployeePosition,

c.ContractType,

c.StartDate

FROM Employee AS e

LEFT OUTER JOIN Contract AS c

ON c.EmployeeID = e.EmployeeID

)

SELECT

ContractType,

COUNT ( DISTINCT EmployeeID) AS TotalEmployees

FROM result

GROUP BY ContractType

HAVING COUNT ( DISTINCT EmployeeID) > 2 ;

Why This is Correct

LEFT OUTER JOIN ensures all employees are included, regardless of contracts.

HAVING allows filtering aggregated groups by employee counts.

The query returns ContractType , StartDate (as requested in denormalization), and aggregated employee counts.

References

T-SQL JOINs in Microsoft Fabric

Aggregate functions and HAVING clause

Denormalization best practices in Fabric warehouses

The suggested replacement does not improve performance. COUNTROWS( ' Order Item ' ) < 0 is always false (row counts cannot be negative), so the NOT will always evaluate to true. This means the filter condition becomes non-selective and would return all customers, not just those with orders. Therefore, it does not meet the optimization goal.

We are optimizing model1 in Fabric with two very large columns:

TransactionKey

Cardinality: 2.5 billion

Size: 160 GB

Description: surrogate key for SalesTransaction fact table.

SaleDateTime

Cardinality: 6.8 million

Size: 120 GB

Description: datetime (to the second) of when a sale occurred.

Goal:

Reduce model size.

Improve refresh performance (Import mode).

Ensure datetime values remain available .

Analysis per column

1. TransactionKey (very high cardinality)

Surrogate keys with extremely high cardinality do not add analytical value in a semantic model because they are not used in measures or grouping.

They take a lot of memory and reduce refresh performance.

Best practice: remove the column from the model.

2. SaleDateTime

Needed to analyze transactions by date and time.

However, storing the full datetime at second-level cardinality causes huge memory usage.

To optimize: split the column into Date and Time columns.

Reduces cardinality significantly.

Preserves full datetime information (can recombine if needed).

Improves compression and refresh speed.

Final Answer:

TransactionKey: Remove the column.

SaleDateTime: Split the column.

ISEMPTY(CALCULATETABLE( ' Order Item ' )) checks if there are no related rows in ' Order Item ' for a customer. Wrapping this in NOT returns true if the customer has at least one order. This avoids calculating row counts and instead just checks existence, which is more efficient for performance. This reduces execution time and meets the goal.

 Calculation groups are a Tabular Object Model (TOM) feature.

 They cannot be authored in Power BI Desktop or the service.

 They are implemented using Tabular Editor .

Comprehensive Detailed Explanation

The question asks: Which syntax should you use in a Fabric notebook to access the Research division data for Productline1?

Key Background from the Case

In Productline1ws , a lakehouse named Lakehouse1 is created.

In Lakehouse1 , a shortcut is created to storage1 , named ResearchProduct .

Storage1 contains the Research division data for Productline1 in Delta format .

Requirement: All data in lakehouses must be presented as managed tables in Lakehouse explorer .

Analyzing the Syntax Options

Option A:

spark.sql( " SELECT * FROM Lakehouse1.ResearchProduct " )

This syntax directly queries the ResearchProduct shortcut within Lakehouse1 using Spark SQL.

Since the shortcut points to Delta data , Spark can directly query it.

This is the correct way to retrieve Productline1 data from Lakehouse1.

Option B:

spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchProduct " )

This introduces an extra schema-like path ( productline1 ) that is not part of the shortcut name .

Incorrect, because the shortcut was created as ResearchProduct inside Lakehouse1, not under another schema.

Option C:

external_table( ' Tables/ResearchProduct ' )

external_table is not the correct way to access a Lakehouse shortcut.

Shortcuts in Lakehouses appear as tables and can be queried using Spark SQL directly.

Option D:

spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchProduct " )

Same issue as Option B, includes a schema path that does not exist.

Correct Choice

Since the shortcut to ResearchProduct was created inside Lakehouse1 , and Spark SQL can query it directly, the correct syntax is:

spark.sql( " SELECT * FROM Lakehouse1.ResearchProduct " )

That matches Option A .

References

Microsoft Fabric Lakehouse – Shortcuts

Query data in a lakehouse using Spark SQL

Delta format support in Microsoft Fabric

You need to recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.

Requirement: “The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.”

Fabric capacity SKUs:

F (Fabric) = dedicated Fabric capacity, available on pay-as-you-go with per-minute billing.

P (Premium) and EM are Power BI capacities (not Fabric-native).

A refers to Azure Analysis Services capacity.

The only correct option is F SKU .

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:

Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.

Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.

Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.

Comprehensive Detailed Explanation

The requirement is to logically group the Research division workspaces and support filtering in the OneLake data hub .

Why Domain ?

Microsoft Fabric allows you to assign workspace domains (e.g., Finance, HR, Research).

This provides a logical grouping of workspaces within the organization.

Domains are the only supported way to enable filtering in OneLake data hub by department .

Capacity or tenant grouping would not allow filtering by department in OneLake.

Why OneLake data hub ?

The OneLake data hub is the centralized interface to browse and discover data assets across the organization.

With workspace domains configured, users can filter directly in the OneLake data hub by domain (e.g., “Research”).

The Fabric Admin portal and Entra admin center are used for administration, not for workspace filtering in data discovery.

Correct Selection for the Hotspot

Grouping method: Domain

Tool: OneLake data hub

References

Workspace domains in Microsoft Fabric

Discover and filter data in OneLake data hub

Final Answer:

Grouping method = Domain

Tool = OneLake data hub

Comprehensive Detailed Explanation

From the case study:

Research division data for Productline2 is currently in CSV format in storage2.

Requirement: “All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.”

In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.

Step 1: Reading the source

df = spark.read. format ( " csv " ) \

options(header= " true " , inferSchema= " true " ) \

load( " abfss://storage1.dfs.core.windows.net/files/productline2 " )

This correctly ingests the CSV source from ADLS Gen2.

Step 2: Writing to Lakehouse as a managed table

You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.

The correct path is under Tables/ , because this is where Fabric automatically manages Lakehouse managed tables .

The target table should be named productline2 , so the correct path is:

df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " )

Why not other options?

CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.

Writing to productline2 directly (without Tables/ ) would store unmanaged files in the Lakehouse Files area, not managed tables.

Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.

Correct Final Code

df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " )

References

Managed tables in Microsoft Fabric Lakehouse

Delta Lake support in Fabric

Spark write to Lakehouse

You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.

Requirement: “All the semantic models and reports for the Research division must use version control that supports branching.”

Fabric supports Git integration (Azure Repos or GitHub) for semantic models and reports.

Storing in Data Lake Gen2 or OneDrive does not provide version control with branching.

The correct action is to integrate workspaces with a Git repository.

Answer: D. Modify the settings of the Research division workspaces to use an Azure Repos repository.

Requirement: “The number of rows added to the Orders table during refreshes must be minimized.”

The Orders table has a sequential OrderID .

To minimize refresh size, you must only import new rows , not the entire table.

This requires retrieving the maximum OrderID currently loaded, then only loading rows with a greater OrderID.

So, the correct solution is to use a pipeline (ADF) with a dataflow to retrieve the maximum OrderID .

Answer: C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse