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Microsoft DP-700 Bundle

Exam Code: DP-700

Exam Name Implementing Data Engineering Solutions Using Microsoft Fabric

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate

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Microsoft DP-700 Practice Test 36

Question 350

A Spark notebook transforms large JSON logs in Fabric. The current environment exhibits the following problem: Spark writes many tiny files and queries slow down. Which approach best satisfies this requirement?

  1. Use a scoped partition overwrite or Delta MERGE for changed records
  2. Optimize Delta file layout after heavy small-batch writes
  3. Deduplicate or constrain dimension rows at the intended join grain
  4. Use an explicit schema and a bad-record handling path during Spark reads

Explanation

Correct Answer

B:

Optimize Delta file layout after heavy small-batch writes. File compaction reduces metadata and file-open overhead.

Incorrect Answers

A:

Partition-specific data arrives late but overwrite deletes all historical partitions. Targeted writes preserve unaffected partitions.

C:

A join multiplies events because the dimension key isn't unique. Join cardinality determines row counts.

D:

Nested JSON fields are malformed in a subset of files. Schema control prevents arbitrary inference drift and lost exceptions.

Question 351

A Fabric Lakehouse pipeline applies T-SQL and PySpark transformations. An acceptance test shows that a data engineer attempts INSERT through a read-only Lakehouse SQL analytics endpoint. Which change most directly resolves this failure?

  1. Stage and transform the files through Lakehouse/Spark before loading analytical tables
  2. Use Delta MERGE with stable keys and matching conditions
  3. Perform the Lakehouse Delta write through Spark or supported OneLake writer
  4. Query the Lakehouse SQL analytics endpoint after table metadata is current

Explanation

Correct Answer

C:

Perform the Lakehouse Delta write through Spark or supported OneLake writer. Lakehouse SQL analytics endpoint supports read querying rather than general table DML.

Incorrect Answers

A:

A dataset includes unstructured files that cannot be turned into Warehouse rows directly. File processing and relational serving are different operations.

B:

A Spark notebook writes duplicate rows because an append runs twice. Idempotent upserts absorb replays without double insertion.

D:

An analyst needs a SQL read over the Spark-created Delta tables. SQL endpoint provides a relational read interface to Lakehouse tables.

Question 352

A Fabric Lakehouse pipeline applies T-SQL and PySpark transformations. The engineer has established the following constraint: a Warehouse needs a set-based update of dimensional records. What is the best engineering response?

  1. Perform the Lakehouse Delta write through Spark or supported OneLake writer
  2. Use supported T-SQL MERGE or UPDATE semantics in the Warehouse
  3. Query the Lakehouse SQL analytics endpoint after table metadata is current
  4. Stage and transform the files through Lakehouse/Spark before loading analytical tables

Explanation

Correct Answer

B:

Use supported T-SQL MERGE or UPDATE semantics in the Warehouse. SQL DML is appropriate for relational warehouse transformations.

Incorrect Answers

A:

A data engineer attempts INSERT through a read-only Lakehouse SQL analytics endpoint. Lakehouse SQL analytics endpoint supports read querying rather than general table DML.

C:

An analyst needs a SQL read over the Spark-created Delta tables. SQL endpoint provides a relational read interface to Lakehouse tables.

D:

A dataset includes unstructured files that cannot be turned into Warehouse rows directly. File processing and relational serving are different operations.

Question 353

A Fabric Lakehouse pipeline applies T-SQL and PySpark transformations. During an operations handoff, engineers find that a Spark notebook writes duplicate rows because an append runs twice. Which action should the team take next?

  1. Stage and transform the files through Lakehouse/Spark before loading analytical tables
  2. Use Delta MERGE with stable keys and matching conditions
  3. Query the Lakehouse SQL analytics endpoint after table metadata is current
  4. Use supported T-SQL MERGE or UPDATE semantics in the Warehouse

Explanation

Correct Answer

B:

Use Delta MERGE with stable keys and matching conditions. Idempotent upserts absorb replays without double insertion.

Incorrect Answers

A:

A dataset includes unstructured files that cannot be turned into Warehouse rows directly. File processing and relational serving are different operations.

C:

An analyst needs a SQL read over the Spark-created Delta tables. SQL endpoint provides a relational read interface to Lakehouse tables.

D:

A Warehouse needs a set-based update of dimensional records. SQL DML is appropriate for relational warehouse transformations.

Question 354

A Fabric Lakehouse pipeline applies T-SQL and PySpark transformations. The team observes that an analyst needs a SQL read over the Spark-created Delta tables. Which approach best satisfies this requirement?

  1. Use Delta MERGE with stable keys and matching conditions
  2. Perform the Lakehouse Delta write through Spark or supported OneLake writer
  3. Query the Lakehouse SQL analytics endpoint after table metadata is current
  4. Stage and transform the files through Lakehouse/Spark before loading analytical tables

Explanation

Correct Answer

C:

Query the Lakehouse SQL analytics endpoint after table metadata is current. SQL endpoint provides a relational read interface to Lakehouse tables.

Incorrect Answers

A:

A Spark notebook writes duplicate rows because an append runs twice. Idempotent upserts absorb replays without double insertion.

B:

A data engineer attempts INSERT through a read-only Lakehouse SQL analytics endpoint. Lakehouse SQL analytics endpoint supports read querying rather than general table DML.

D:

A dataset includes unstructured files that cannot be turned into Warehouse rows directly. File processing and relational serving are different operations.

Question 355

A Fabric Lakehouse pipeline applies T-SQL and PySpark transformations. The implementation review confirms that a dataset includes unstructured files that cannot be turned into Warehouse rows directly. Which corrective action addresses the root cause?

  1. Stage and transform the files through Lakehouse/Spark before loading analytical tables
  2. Query the Lakehouse SQL analytics endpoint after table metadata is current
  3. Use Delta MERGE with stable keys and matching conditions
  4. Use supported T-SQL MERGE or UPDATE semantics in the Warehouse

Explanation

Correct Answer

A:

Stage and transform the files through Lakehouse/Spark before loading analytical tables. File processing and relational serving are different operations.

Incorrect Answers

B:

An analyst needs a SQL read over the Spark-created Delta tables. SQL endpoint provides a relational read interface to Lakehouse tables.

C:

A Spark notebook writes duplicate rows because an append runs twice. Idempotent upserts absorb replays without double insertion.

D:

A Warehouse needs a set-based update of dimensional records. SQL DML is appropriate for relational warehouse transformations.

Question 356

A Fabric ingestion design uses OneLake shortcuts for shared data. A scheduled execution exposes this issue: cross-team Lakehouse readers need current Delta data without a copied dataset. Which change most directly resolves this failure?

  1. Create an internal OneLake shortcut to the source table where supported
  2. Evaluate appropriate query caching, acceleration, or materialization for its consumer engine
  3. Configure approved delegated connection authentication and permissions
  4. Grant the required data permissions on shortcut and target paths

Explanation

Correct Answer

A:

Create an internal OneLake shortcut to the source table where supported. Shortcuts reference existing data and avoid replica freshness lag.

Incorrect Answers

B:

An external shortcut is slow for a complex repeated query. Remote reads can incur latency that requires engine-specific optimization.

C:

A shortcut's external connection lacks source-system credentials. External sources are accessed with stored delegated credentials.

D:

A viewer can see an internal shortcut name but cannot open its target. Shortcut listing and data read authorization are separate.

Question 357

A Fabric ingestion design uses OneLake shortcuts for shared data. Data validation establishes that a shortcut's external connection lacks source-system credentials. What is the best engineering response?

  1. Create an internal OneLake shortcut to the source table where supported
  2. Configure approved delegated connection authentication and permissions
  3. Evaluate appropriate query caching, acceleration, or materialization for its consumer engine
  4. Materialize the transformed data into a managed target table

Explanation

Correct Answer

B:

Configure approved delegated connection authentication and permissions. External sources are accessed with stored delegated credentials.

Incorrect Answers

A:

Cross-team Lakehouse readers need current Delta data without a copied dataset. Shortcuts reference existing data and avoid replica freshness lag.

C:

An external shortcut is slow for a complex repeated query. Remote reads can incur latency that requires engine-specific optimization.

D:

A team wants to transform and permanently own a derived copy rather than view the source. Shortcuts are pointers and do not produce independent transformed datasets.

Question 358

A Fabric ingestion design uses OneLake shortcuts for shared data. The production incident report states that a viewer can see an internal shortcut name but cannot open its target. Which action should the team take next?

  1. Grant the required data permissions on shortcut and target paths
  2. Create an internal OneLake shortcut to the source table where supported
  3. Evaluate appropriate query caching, acceleration, or materialization for its consumer engine
  4. Configure approved delegated connection authentication and permissions

Explanation

Correct Answer

A:

Grant the required data permissions on shortcut and target paths. Shortcut listing and data read authorization are separate.

Incorrect Answers

B:

Cross-team Lakehouse readers need current Delta data without a copied dataset. Shortcuts reference existing data and avoid replica freshness lag.

C:

An external shortcut is slow for a complex repeated query. Remote reads can incur latency that requires engine-specific optimization.

D:

A shortcut's external connection lacks source-system credentials. External sources are accessed with stored delegated credentials.

Question 359

A Fabric ingestion design uses OneLake shortcuts for shared data. A postdeployment check reveals that a team wants to transform and permanently own a derived copy rather than view the source. Which approach best satisfies this requirement?

  1. Create an internal OneLake shortcut to the source table where supported
  2. Materialize the transformed data into a managed target table
  3. Grant the required data permissions on shortcut and target paths
  4. Configure approved delegated connection authentication and permissions

Explanation

Correct Answer

B:

Materialize the transformed data into a managed target table. Shortcuts are pointers and do not produce independent transformed datasets.

Incorrect Answers

A:

Cross-team Lakehouse readers need current Delta data without a copied dataset. Shortcuts reference existing data and avoid replica freshness lag.

C:

A viewer can see an internal shortcut name but cannot open its target. Shortcut listing and data read authorization are separate.

D:

A shortcut's external connection lacks source-system credentials. External sources are accessed with stored delegated credentials.

Question 360

A Fabric ingestion design uses OneLake shortcuts for shared data. The current environment exhibits the following problem: an external shortcut is slow for a complex repeated query. Which corrective action addresses the root cause?

  1. Configure approved delegated connection authentication and permissions
  2. Evaluate appropriate query caching, acceleration, or materialization for its consumer engine
  3. Materialize the transformed data into a managed target table
  4. Create an internal OneLake shortcut to the source table where supported

Explanation

Correct Answer

B:

Evaluate appropriate query caching, acceleration, or materialization for its consumer engine. Remote reads can incur latency that requires engine-specific optimization.

Incorrect Answers

A:

A shortcut's external connection lacks source-system credentials. External sources are accessed with stored delegated credentials.

C:

A team wants to transform and permanently own a derived copy rather than view the source. Shortcuts are pointers and do not produce independent transformed datasets.

D:

Cross-team Lakehouse readers need current Delta data without a copied dataset. Shortcuts reference existing data and avoid replica freshness lag.