Microsoft Fabric Data Engineer Associate

DP-700 Practice Test

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DP-700

Microsoft Fabric Data Engineer Associate

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Today's 10 DP-700 questions

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Question 1 of 10

Objective Delta optimization Monitor and optimize an analytics solution

A Lakehouse Delta table stores sales history, and reports must always keep the last seven years of data available. How should older, unused files be cleaned up?

Concept tested:

Source:Delta Lake VACUUM operation in Fabric

Question 2 of 10

Objective Direct Lake Ingest and transform data

A Direct Lake semantic model is unexpectedly falling back to DirectQuery. What should the team run first to identify which table is falling back and why?

Concept tested:

Question 3 of 10

Objective Security and governance Implement and manage an analytics solution

A stakeholder requires read-only access to view content within a Fabric workspace, without the ability to create, modify, or manage items. Which workspace role should be assigned to this user?

Concept tested:

Question 4 of 10

Objective Query performance Monitor and optimize an analytics solution

A Fabric Warehouse query is slow, and the engineer needs to identify join and scan bottlenecks before rewriting the SQL. What should be reviewed first to understand the query's execution plan and potential performance issues?

Concept tested:

Question 5 of 10

Objective Lakehouses Ingest and transform data

A Bronze layer receives raw JSON files that need parsing, schema validation, and basic type conversions at scale. Which Fabric tool is best suited for this transformation step?

Concept tested:

Question 6 of 10

Objective OneLake Shortcuts Implement and manage an analytics solution

Workspace A needs to access sensitive data in Workspace B without duplicating the dataset. What should be created to establish a secure data reference?

Concept tested:

Question 7 of 10

Objective Monitoring Spark jobs Monitor and optimize an analytics solution

A Spark job is slow, and the team needs to find expensive stages, shuffle activity, and executor behavior. Which Spark monitoring view should they inspect to identify performance bottlenecks in the Spark job execution?

Concept tested:

Question 8 of 10

Objective Stream Processing Ingest and transform data

A clickstream Eventstream must handle high event volume, enrich events, and route the enriched stream to both Power BI and a data lake. Which design is appropriate?

Concept tested:

Question 9 of 10

Objective Lifecycle management Implement and manage an analytics solution

A Fabric team needs separate Development, Staging, and Production stages with different user permissions and parameter overrides during promotion. What deployment design meets those needs?

Concept tested:

Question 10 of 10

Objective Spark optimization Monitor and optimize an analytics solution

A Fabric engineering team needs one Delta table to keep a fixed 256 MB file target for controlled benchmark testing, even though adaptive target file size is enabled in the Spark session. What should be configured?

Concept tested:

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Certification Microsoft Certified: Fabric Data Engineer Associate Study guide DP-700 study guide Docs Microsoft Fabric documentation

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Question 1 A Lakehouse Delta table stores sales history, and reports must always keep the last seven years of data available. How should older, unused files be cleaned up?

Answer choices

A. Increase the Spark pool size to improve compaction and automatically manage file retention
B. Implement a scheduled Dataflow Gen2 job that periodically deletes partitions based on their creation date
C. Configure a Delta Lake VACUUM operation with a retention period of 7 years to remove files older than that date
D. Modify the Delta Lake table's schema to include a timestamp column and filter data based on that column in all queries

Correct answer

Configure a Delta Lake VACUUM operation with a retention period of 7 years to remove files older than that date

Delta VACUUM is used to remove old files according to a retention policy after they are no longer needed. A seven-year retention period aligns cleanup with the requirement to keep the last seven years available for reporting. Spark pool sizing, manual partition deletion, and query filters do not provide the same Delta file cleanup policy. That makes this the appropriate choice: Configure a Delta Lake VACUUM operation with a retention period of 7 years to remove files older than that date.

Wrong-answer review

A. Increase the Spark pool size to improve compaction and automatically manage file retention: Spark pool size affects compute capacity and does not automatically enforce retention cleanup.
B. Implement a scheduled Dataflow Gen2 job that periodically deletes partitions based on their creation date: A scheduled Dataflow deletion job risks bypassing Delta table retention semantics.
D. Modify the Delta Lake table's schema to include a timestamp column and filter data based on that column in all queries: Adding a timestamp column and filtering queries leaves old files in storage.

Objective/domain: Monitor and optimize an analytics solution

Source: Delta Lake VACUUM operation in Fabric

Question 2 A Direct Lake semantic model is unexpectedly falling back to DirectQuery. What should the team run first to identify which table is falling back and why?

Answer choices

A. Review the Delta table partition scheme and Z-Ordering configuration before checking fallback details
B. Run EVALUATE TABLETRAITS() and review the DirectLakeFallbackInfo column
C. Check the Fabric workspace Spark pool memory and executor settings
D. Review Delta table schema evolution history for unsupported Spark data types

Correct answer

Run EVALUATE TABLETRAITS() and review the DirectLakeFallbackInfo column

Objective/domain: Ingest and transform data

Source: How Direct Lake works

Question 3 A stakeholder requires read-only access to view content within a Fabric workspace, without the ability to create, modify, or manage items. Which workspace role should be assigned to this user?

Answer choices

A. Contributor
B. Viewer
C. Member
D. Admin

Correct answer

Viewer

Objective/domain: Implement and manage an analytics solution

Source: Roles in Microsoft Fabric workspaces

Question 4 A Fabric Warehouse query is slow, and the engineer needs to identify join and scan bottlenecks before rewriting the SQL. What should be reviewed first to understand the query's execution plan and potential performance issues?

Answer choices

A. Manually rewrite the T-SQL query to use different join algorithms before reviewing the optimizer's plan
B. Use SHOWPLAN_XML in SQL Server Management Studio to review the query execution plan and identify scan, join, or non-scalable operations
C. Add query hints immediately to force a specific execution plan before reviewing the query behavior
D. Increase the Fabric capacity size before identifying whether the query is affected by plan shape, cold cache, or data volume

Correct answer

Use SHOWPLAN_XML in SQL Server Management Studio to review the query execution plan and identify scan, join, or non-scalable operations

Objective/domain: Monitor and optimize an analytics solution

Source: Performance guidelines in Fabric Data Warehouse

Question 5 A Bronze layer receives raw JSON files that need parsing, schema validation, and basic type conversions at scale. Which Fabric tool is best suited for this transformation step?

Answer choices

A. Utilize a Power BI Dataflow to perform the initial cleansing and transformation due to its user-friendly interface and visual data profiling capabilities
B. Create a Synapse SQL script to ingest and transform the data, leveraging SQL's declarative nature for data manipulation
C. Employ a Spark Notebook with PySpark to handle the raw JSON parsing, schema validation, and basic data type conversions, leveraging Spark's distributed processing capabilities
D. Use a combination of Azure Functions and Azure Logic Apps to orchestrate the data ingestion and transformation process, providing flexibility and scalability

Correct answer

Employ a Spark Notebook with PySpark to handle the raw JSON parsing, schema validation, and basic data type conversions, leveraging Spark's distributed processing capabilities

Objective/domain: Ingest and transform data

Source: Lakehouse end-to-end scenario: overview and architecture

Question 6 Workspace A needs to access sensitive data in Workspace B without duplicating the dataset. What should be created to establish a secure data reference?

Answer choices

A. Create an internal OneLake shortcut in Workspace A that points to the sensitive dataset in Workspace B
B. Grant the report developers in Workspace A a broad authoring role on the source dataset
C. Export the sensitive data to Workspace A on a schedule
D. Update source-side authorization so Workspace A users can open the dataset

Correct answer

Create an internal OneLake shortcut in Workspace A that points to the sensitive dataset in Workspace B

Objective/domain: Implement and manage an analytics solution

Source: Create a OneLake shortcut

Question 7 A Spark job is slow, and the team needs to find expensive stages, shuffle activity, and executor behavior. Which Spark monitoring view should they inspect to identify performance bottlenecks in the Spark job execution?

Answer choices

A. Examine the Spark History Server's 'Jobs' tab, specifically looking at the overall job duration and the number of tasks completed
B. Check the Fabric workspace's auto-scaling settings for the default Spark pool to ensure sufficient resources are allocated
C. Analyze the Spark History Server's 'Applications' tab to identify the user who submitted the job and their associated permissions
D. Review the Spark History Server to analyze the 'Stages' tab, focusing on task durations, shuffle read/write times, and executor metrics for each stage

Correct answer

Review the Spark History Server to analyze the 'Stages' tab, focusing on task durations, shuffle read/write times, and executor metrics for each stage

Objective/domain: Monitor and optimize an analytics solution

Source: Use extended Apache Spark history server to debug and diagnose Apache Spark applications

Question 8 A clickstream Eventstream must handle high event volume, enrich events, and route the enriched stream to both Power BI and a data lake. Which design is appropriate?

Answer choices

A. Use one Eventstream with one partition and route through a notebook
B. Use multiple Eventstream objects with an enrichment handoff
C. Use one multi-partition Eventstream with enrichment and two destinations
D. Use Dataflow Gen2 as the enrichment layer for the stream

Correct answer

Use one multi-partition Eventstream with enrichment and two destinations

Objective/domain: Ingest and transform data

Source: Configure settings for a Fabric eventstream

Question 9 A Fabric team needs separate Development, Staging, and Production stages with different user permissions and parameter overrides during promotion. What deployment design meets those needs?

Answer choices

A. Grant all workspace members 'Contributor' access and utilize Git branching strategies to isolate development efforts, then manually merge branches into Production after UAT
B. Create separate Microsoft Fabric workspaces for Development, Staging, and Production, and manually copy items between them using the Fabric UI
C. Configure a deployment pipeline with three stages (Development, Staging, Production), assign different user groups to each stage with varying permission levels (e.g., 'Contributor' for Development, 'Reader' for Production), and utilize pipeline rules to manage parameter overrides
D. Enable workspace-level data policies to restrict access to sensitive data in the Production environment, while granting all users 'Contributor' access to all stages

Correct answer

Configure a deployment pipeline with three stages (Development, Staging, Production), assign different user groups to each stage with varying permission levels (e.g., 'Contributor' for Development, 'Reader' for Production), and utilize pipeline rules to manage parameter overrides

Objective/domain: Implement and manage an analytics solution

Source: Understand the deployment pipeline process

Question 10 A Fabric engineering team needs one Delta table to keep a fixed 256 MB file target for controlled benchmark testing, even though adaptive target file size is enabled in the Spark session. What should be configured?

Answer choices

A. Set the table property delta.targetFileSize to 256m
B. Increase the number of Spark partitions to distribute the write load more evenly
C. Set spark.sql.shuffle.partitions to a higher value for the notebook session
D. Modify spark.driver.maxResultSize to accommodate larger data transfers

Correct answer

Set the table property delta.targetFileSize to 256m

Objective/domain: Monitor and optimize an analytics solution

Source: Tune File Size

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