Microsoft Certified: Fabric Data Engineer Associate (DP-700) Exam Questions

Correct Answer:

B) Configure the Output destination to explicitly map the transformed output to a specific table in the Lakehouse.

Explanation:

In Microsoft Fabric's Dataflow Gen 2, transformed data needs to be explicitly mapped to a Lakehouse table or another destination. If the output mapping is not configured correctly, the dataflow will run without errors, but the transformed data will not be stored where expected.

• Option A (Incorrect): "Staging Only" mode allows data to persist temporarily but does not ensure it gets loaded into the Lakehouse.

• Option C (Incorrect): "Append Only" mode prevents overwriting but does not guarantee that data is mapped correctly to the Lakehouse.

• Option D (Incorrect): While Direct Lake Mode optimizes query performance, it does not solve the issue of missing data due to incorrect mapping.

Thus, explicitly configuring the Output destination in the dataflow settings is necessary to ensure that transformed data reaches the intended Lakehouse table.

For detailed guidance, refer to the official Microsoft documentation:

• Dataflow Gen2 data destinations and managed settings

• Copy sample data into Lakehouse and transform with a dataflow

Correct Answer:

B) Configure a Retry Policy in the pipeline settings with an appropriate retry count and interval.

Explanation:

Microsoft Fabric Data Pipelines support retry policies to handle failures in execution. If a dataflow fails due to transient issues (e.g., connectivity problems, temporary service unavailability), enabling retries ensures the pipeline automatically attempts to rerun the step before marking it as failed.

• Option A (Incorrect): A schedule-based trigger does not address failures—it only ensures periodic execution.

• Option C (Incorrect): While a conditional activity could detect failure, it requires additional logic, making it less efficient than using built-in retry policies.

• Option D (Incorrect): Direct Lake Mode optimizes data retrieval but does not handle retry mechanisms in the pipeline.

By enabling a Retry Policy in the Data Pipeline settings, you ensure resilience and minimize data disruptions in case of transient failures.

For detailed guidance, refer to the official Microsoft documentation:

• Activity overview - Microsoft Fabric

Answers:

B and D

Explanation:

• Option A: This command uses .create to define a materialized view with a specified folder. However, if a materialized view named AggregatedData already exists, this command will fail because .create does not allow for altering existing views.

• Option B: This command utilizes .create-or-alter, which either creates a new materialized view or alters an existing one with the same name. The with (folder="Gold") clause specifies the folder where the view is organized, aiding in data management and organization. This option is correct.

• Option C: Similar to Option A, this command uses .create without specifying a folder. While it will successfully create the materialized view if it doesn't already exist, it will fail if a view named AggregatedData exists, as .create cannot alter existing views.

• Option D: This command uses .create-or-alter without specifying a folder. It will create a new materialized view or alter an existing one named AggregatedData. While it functions correctly, it lacks the organizational benefit of assigning the view to a specific folder.

Therefore, Options B and D are correct because they use .create-or-alter, ensuring the materialized view is created or updated as needed. Option B is preferable if you want to organize the view within a specific folder, enhancing data management.

For more details on creating materialized views in Kusto, refer to the official Microsoft documentation:

• Create materialized view - Kusto

Answers:

B, C, and D

Explanation:

❌ A. Enable the "Create Real-Time Dashboards" setting in the Microsoft Fabric admin portal.

• This option is not required. As of now, there is no specific admin setting in Microsoft Fabric named "Create Real-Time Dashboards" that needs to be enabled manually.

• Real-Time Dashboards can be created by users with appropriate workspace access without additional admin configuration.

✅ Explanation of Correct Steps:

B. Create a Real-Time Dashboard

• Go to your Microsoft Fabric workspace.

• Click "New" > "Real-Time Dashboard".

• This is the container for your live monitoring visuals.

C. Add a data source (KQL database)

• In the Real-Time Dashboard, connect to the KQL database (powered by OneLake or Azure Data Explorer).

• This enables real-time querying and visualization of data streams or log data.

D. Write and add KQL query tiles

• Use the KQL query editor inside the Real-Time Dashboard interface to write a query.

• Save the output as a tile, which will be refreshed in near real-time on the dashboard.

✅ Summary:

To build a Real-Time Dashboard in Microsoft Fabric:

• Create the dashboard (B)

• Connect to the KQL data source (C)

• Add tiles using KQL queries (D)

​To create and visualize data on a Real-Time Dashboard in Microsoft Fabric using a KQL database, you can follow the steps outlined in the official Microsoft documentation:​

• Create a Real-Time Dashboard - Microsoft Fabric

Correct Answer:

B) Configure an Incremental Refresh Policy in the Dataflow settings to only process new and changed records.

Explanation:

Incremental Refresh in Dataflow Gen 2 is designed to improve performance by processing only new and updated records instead of reloading the entire dataset.

• Option A (Incorrect): While Query Folding optimizes transformations by pushing them to the source, it does not control which records are refreshed in each cycle.

• Option C (Incorrect): Manually storing a timestamp for tracking changes is possible but not the recommended approach, as Incremental Refresh provides a more automated and scalable solution.

• Option D (Incorrect): "Append Only" mode prevents overwriting but does not ensure that only recent data is processed—it may still reload older records unnecessarily.

By enabling Incremental Refresh in Dataflow Gen 2, you ensure that only new and modified records are processed, reducing compute costs and improving refresh efficiency.

For detailed guidance, refer to the official Microsoft documentation:

• Incremental refresh in Dataflow Gen2 - Microsoft Fabric

This article provides comprehensive instructions on setting up incremental refresh, including:​

• Choosing a DateTime column to filter by.

• Defining the data extraction range (e.g., transactions from the last 12 months).

• Setting the bucket size for data segmentation.

• Configuring advanced settings to ensure optimal performance

Correct Answer:

B) Configure an Event-based trigger in the pipeline settings that listens to the ADLS Gen2 storage event for file creation.

Explanation:

Microsoft Fabric Data Pipelines support event-based triggers, allowing the pipeline to execute automatically when new files arrive in ADLS Gen2. If the pipeline is not triggering, it likely lacks the correct event-based trigger configuration.

• Option A (Incorrect): A scheduled trigger runs at fixed intervals but does not ensure immediate execution when a new file arrives.

• Option C (Incorrect): Auto Retry helps with transient failures but does not address missing triggers.

• Option D (Incorrect): Polling is inefficient and not natively supported in Fabric Data Pipelines; an event-driven approach is more optimal.

By configuring an Event-based trigger in the pipeline settings, the pipeline can automatically detect new file uploads in ADLS Gen2 and execute the required transformations, ensuring timely data processing.

For detailed guidance, refer to the official Microsoft documentation:

• Data pipelines storage event triggers in Data Factory - Microsoft Fabric

Correct Answer:

✅ B) Correct Sequence of Steps:

1️⃣ Create a Fabric Data Pipeline and define activities (Copy Data, Dataflow Gen 2, and Lakehouse Load).
2️⃣ Configure an Event-Based Trigger on ADLS Gen2 to detect file uploads.
3️⃣ Activate the Trigger and Test by uploading a new file.
4️⃣ Validate that the transformed data appears in the Lakehouse.

Explanation:

• Step 1: You must first create the Fabric Data Pipeline and define the sequence of activities (Copy Data, Dataflow Gen 2, Lakehouse Load).

• Step 2: Once the pipeline structure is ready, you configure an Event-Based Trigger on ADLS Gen2 to automate execution.

• Step 3: After activating the trigger, you test it by uploading a new file and verifying that the pipeline runs as expected.

• Step 4: Finally, you validate the processed data in the Lakehouse table to ensure it has been ingested correctly.

Documentation: Data pipelines storage event triggers in Data Factory - Microsoft Fabric

Documentation: Use a dataflow in a pipeline - Microsoft Fabric

Documentation: Incremental refresh in Dataflow Gen2 - Microsoft Fabric

Documentation: Copy sample data into Lakehouse and transform with dataflow - Microsoft Fabric

Correct Answer:

A) Enable the Auto-mapping feature in Copy Data activity to dynamically adjust for schema changes.

📌 Explanation:

• Auto-mapping allows Copy Data to dynamically update column mappings when schema changes occur.

• Option B (Incorrect): Dataflow Gen 2 transformations do not control how the Copy Data activity handles schema changes.

• Option C (Incorrect): Retry policies only re-execute the failed pipeline but do not handle schema evolution.

• Option D (Incorrect): Direct Lake Mode improves query performance but does not affect schema handling in Copy Data activity.

Reference:

• How to handle schema drift in Dataflow Gen2 - Microsoft Fabric

• Schema and data type mapping in copy activity - Azure Data Factory

The correct answer is:

✅ B) Create a new pipeline from the template, then customize the transformation logic before deployment.

Explanation:

To leverage the benefits of a pre-built template (standardization, faster development, reusable structure) while tailoring it to your specific business requirements, the best approach is:

• Create a new pipeline from the template.

• Customize components like the Dataflow Gen2 activity or transformation steps to reflect your specific logic.

• Then deploy the updated version.

This keeps the original template intact for reuse by others and allows controlled customization for your use case.

❌ Why the other options are not ideal:

A) Edit the existing pipeline template directly in the Fabric Data Pipeline editor and save changes

• 🔴 Incorrect because templates are not designed to be edited directly.

• Once a pipeline is generated from a template, that instance can be edited, but not the template itself.

C) Modify the source template file in Microsoft Fabric’s template repository so all pipelines use the new logic

• 🔴 Risky and not recommended unless you intend to change the logic for all teams/orgs using that template.

• Breaks standardization if not carefully governed.

D) Use Dynamic Content Expressions to alter transformations at runtime without modifying the pipeline template

• 🔴 Incorrect because transformation logic (e.g., in Dataflow Gen2) cannot be fully handled by dynamic expressions.

• This is more suited for parameters in pipeline control flows, not data transformations.

✅ Final Answer:

B) Create a new pipeline from the template, then customize the transformation logic before deployment.

For detailed guidance, refer to the official Microsoft documentation:

• Templates for Data Factory in Microsoft Fabric

This resource provides comprehensive instructions on how to use and customize pipeline templates within Microsoft Fabric.

Correct Answer:

✅ A) Open the Pipeline Run Details page and check the Activity Run history to find errors or warnings.

Explanation:

When troubleshooting pipeline execution issues, the first step is to check the Monitoring tab to inspect Pipeline Run Details and Activity Run history for error messages.

• Option A (Correct): Reviewing the Pipeline Run Details and Activity Run history helps pinpoint where issues occurred and provides logs for debugging.

• Option B (Incorrect): Increasing Parallel Copy settings may improve performance, but it does not address warnings or missing data.

• Option C (Incorrect): Enabling Fault Tolerance allows execution to continue but does not help diagnose the issue.

• Option D (Incorrect): Re-running from the last failed step is useful after identifying the issue, but first, you need to find the root cause.

By using Fabric’s monitoring tools, you can analyze warnings and failures, ensuring accurate data ingestion into the Warehouse.

Refer to the troubleshooting guide for more information:​

Troubleshoot Pipelines in Microsoft Fabric