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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 35

Question 340

A pipeline Copy activity loads data from an on-premises SQL source. The current environment exhibits the following problem: a pipeline Copy succeeds but required transforms are never executed. Which option directly handles this observed condition?

  1. Add a dependent transformation activity after the copy completes
  2. Use a controlled incremental extraction and idempotent destination merge
  3. Verify supported gateway connectivity and database network access
  4. Define and validate Copy activity column mappings and data types

Explanation

Correct Answer

A:

Add a dependent transformation activity after the copy completes. Moving bytes is different from applying business logic.

Incorrect Answers

B:

The copy repeats all historic rows on every retry. Copy execution by itself doesn't guarantee exactly-once publishing.

C:

The copy fails because the self-hosted network gateway cannot reach the source. A connector cannot move data without a permitted source route.

D:

The sink schema contains decimal columns but mapping treats them as strings. Explicit mappings avoid lossy or invalid coercion.

Question 341

A low-code engineering team transforms tabular sources with Dataflow Gen2. An acceptance test shows that a source's date column imports as text and breaks month grouping. Which corrective action addresses the root cause?

  1. Assign the intended date type in the Power Query steps
  2. Update the step dependency and handle expected schema changes
  3. Configure and verify the supported destination mapping and refresh activity
  4. Correct join keys and verify duplicate/missing match behavior

Explanation

Correct Answer

A:

Assign the intended date type in the Power Query steps. Dataflow type conversions must precede date-aware calculations.

Incorrect Answers

B:

A Power Query step references a renamed source field and refresh fails. Power Query transformations bind named fields from earlier stages.

C:

A Dataflow runs but no destination table is refreshed. Successful transformation authoring alone doesn't write the intended sink.

D:

A query merges orders with customers but uses the wrong join cardinality. Merge operations can multiply or discard records when keys aren't unique.

Question 342

A low-code engineering team transforms tabular sources with Dataflow Gen2. The engineer has established the following constraint: duplicate customer rows inflate downstream aggregates. What should the administrator implement or verify?

  1. Apply a business-key deduplication transformation before loading
  2. Configure and verify the supported destination mapping and refresh activity
  3. Assign the intended date type in the Power Query steps
  4. Correct join keys and verify duplicate/missing match behavior

Explanation

Correct Answer

A:

Apply a business-key deduplication transformation before loading. Repeated source keys should be resolved at the correct grain.

Incorrect Answers

B:

A Dataflow runs but no destination table is refreshed. Successful transformation authoring alone doesn't write the intended sink.

C:

A source's date column imports as text and breaks month grouping. Dataflow type conversions must precede date-aware calculations.

D:

A query merges orders with customers but uses the wrong join cardinality. Merge operations can multiply or discard records when keys aren't unique.

Question 343

A low-code engineering team transforms tabular sources with Dataflow Gen2. During an operations handoff, engineers find that a query merges orders with customers but uses the wrong join cardinality. Which option directly handles this observed condition?

  1. Assign the intended date type in the Power Query steps
  2. Update the step dependency and handle expected schema changes
  3. Apply a business-key deduplication transformation before loading
  4. Correct join keys and verify duplicate/missing match behavior

Explanation

Correct Answer

D:

Correct join keys and verify duplicate/missing match behavior. Merge operations can multiply or discard records when keys aren't unique.

Incorrect Answers

A:

A source's date column imports as text and breaks month grouping. Dataflow type conversions must precede date-aware calculations.

B:

A Power Query step references a renamed source field and refresh fails. Power Query transformations bind named fields from earlier stages.

C:

Duplicate customer rows inflate downstream aggregates. Repeated source keys should be resolved at the correct grain.

Question 344

A low-code engineering team transforms tabular sources with Dataflow Gen2. The team observes that a Power Query step references a renamed source field and refresh fails. What is the most defensible next action?

  1. Configure and verify the supported destination mapping and refresh activity
  2. Correct join keys and verify duplicate/missing match behavior
  3. Assign the intended date type in the Power Query steps
  4. Update the step dependency and handle expected schema changes

Explanation

Correct Answer

D:

Update the step dependency and handle expected schema changes. Power Query transformations bind named fields from earlier stages.

Incorrect Answers

A:

A Dataflow runs but no destination table is refreshed. Successful transformation authoring alone doesn't write the intended sink.

B:

A query merges orders with customers but uses the wrong join cardinality. Merge operations can multiply or discard records when keys aren't unique.

C:

A source's date column imports as text and breaks month grouping. Dataflow type conversions must precede date-aware calculations.

Question 345

A low-code engineering team transforms tabular sources with Dataflow Gen2. The implementation review confirms that a Dataflow runs but no destination table is refreshed. Which technical choice fits this evidence?

  1. Configure and verify the supported destination mapping and refresh activity
  2. Assign the intended date type in the Power Query steps
  3. Correct join keys and verify duplicate/missing match behavior
  4. Apply a business-key deduplication transformation before loading

Explanation

Correct Answer

A:

Configure and verify the supported destination mapping and refresh activity. Successful transformation authoring alone doesn't write the intended sink.

Incorrect Answers

B:

A source's date column imports as text and breaks month grouping. Dataflow type conversions must precede date-aware calculations.

C:

A query merges orders with customers but uses the wrong join cardinality. Merge operations can multiply or discard records when keys aren't unique.

D:

Duplicate customer rows inflate downstream aggregates. Repeated source keys should be resolved at the correct grain.

Question 346

A Spark notebook transforms large JSON logs in Fabric. A scheduled execution exposes this issue: nested JSON fields are malformed in a subset of files. Which step preserves the intended data behavior?

  1. Use an explicit schema and a bad-record handling path during Spark reads
  2. Replace collect-heavy logic with distributed DataFrame transformations
  3. Use a scoped partition overwrite or Delta MERGE for changed records
  4. Optimize Delta file layout after heavy small-batch writes

Explanation

Correct Answer

A:

Use an explicit schema and a bad-record handling path during Spark reads. Schema control prevents arbitrary inference drift and lost exceptions.

Incorrect Answers

B:

The notebook builds huge driver-side lists and runs out of memory. Large datasets should remain partitioned across executors.

C:

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

D:

Spark writes many tiny files and queries slow down. File compaction reduces metadata and file-open overhead.

Question 347

A Spark notebook transforms large JSON logs in Fabric. Data validation establishes that the notebook builds huge driver-side lists and runs out of memory. Which change most directly resolves this failure?

  1. Use an explicit schema and a bad-record handling path during Spark reads
  2. Replace collect-heavy logic with distributed DataFrame transformations
  3. Use a scoped partition overwrite or Delta MERGE for changed records
  4. Optimize Delta file layout after heavy small-batch writes

Explanation

Correct Answer

B:

Replace collect-heavy logic with distributed DataFrame transformations. Large datasets should remain partitioned across executors.

Incorrect Answers

A:

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

C:

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

D:

Spark writes many tiny files and queries slow down. File compaction reduces metadata and file-open overhead.

Question 348

A Spark notebook transforms large JSON logs in Fabric. The production incident report states that a join multiplies events because the dimension key isn't unique. What is the best engineering response?

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

Explanation

Correct Answer

D:

Deduplicate or constrain dimension rows at the intended join grain. Join cardinality determines row counts.

Incorrect Answers

A:

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

B:

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

C:

Spark writes many tiny files and queries slow down. File compaction reduces metadata and file-open overhead.

Question 349

A Spark notebook transforms large JSON logs in Fabric. A postdeployment check reveals that partition-specific data arrives late but overwrite deletes all historical partitions. Which action should the team take next?

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

Explanation

Correct Answer

D:

Use a scoped partition overwrite or Delta MERGE for changed records. Use a scoped partition overwrite or Delta MERGE for changed records is the strongest answer for the reported condition: partition-specific data arrives late but overwrite deletes all historical partitions.

Incorrect Answers

A:

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

B:

Spark writes many tiny files and queries slow down. File compaction reduces metadata and file-open overhead.

C:

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

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.