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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 71

Question 700

A Fabric admin investigates jobs failing under constrained capacity. The current environment exhibits the following problem: a Fabric capacity is healthy but an external source throttles extraction. What is the most defensible next action?

  1. Correlate executor failures and admission delays with capacity metrics
  2. Examine query plan costs, concurrent demand and capacity utilization
  3. Check ingest throughput, compute pressure and destination limits
  4. Examine source-specific HTTP or database throttling telemetry

Explanation

Correct Answer

D:

Examine source-specific HTTP or database throttling telemetry. External service limits are not resolved solely by increasing Fabric capacity.

Incorrect Answers

A:

Spark jobs fail at busy hours while idle periods work normally. Saturation may be transient rather than a code defect.

B:

Warehouse query timeouts spike when many scans overlap. Shared resource contention affects query latency.

C:

An Eventhouse backlog grows faster than consumers can process events. Sustained arrival rates can exceed provisioned processing capacity.

Question 701

A regulated organization needs reproducible error recovery. An acceptance test shows that a pipeline's data load fails after writing half of a target partition. Which step preserves the intended data behavior?

  1. Deduplicate by original event identity in the serving model
  2. Restore compatible contracts or deploy consumer fixes with tested rollback
  3. Restore or replace the affected partition using a validated checkpointed plan
  4. Follow the configured retention/backup recovery policy and prevent premature vacuuming

Explanation

Correct Answer

C:

Restore or replace the affected partition using a validated checkpointed plan. Recovery must account for partial writes.

Incorrect Answers

A:

An Eventhouse reingestion produces duplicate records after a replay. Replay should not generate multiple business events.

B:

A schema migration breaks downstream reports after deployment. Schema and consumer versions must be coordinated.

D:

A lakehouse VACUUM removed files needed for the expected recovery point. Delta time travel depends on available historical files.

Question 702

A regulated organization needs reproducible error recovery. The engineer has established the following constraint: a CDC consumer misses events after source retention expires. Which change most directly resolves this failure?

  1. Deduplicate by original event identity in the serving model
  2. Reconcile source state and perform a controlled full backfill
  3. Follow the configured retention/backup recovery policy and prevent premature vacuuming
  4. Restore compatible contracts or deploy consumer fixes with tested rollback

Explanation

Correct Answer

B:

Reconcile source state and perform a controlled full backfill. Expired change logs require a resynchronization strategy.

Incorrect Answers

A:

An Eventhouse reingestion produces duplicate records after a replay. Replay should not generate multiple business events.

C:

A lakehouse VACUUM removed files needed for the expected recovery point. Delta time travel depends on available historical files.

D:

A schema migration breaks downstream reports after deployment. Schema and consumer versions must be coordinated.

Question 703

A regulated organization needs reproducible error recovery. During an operations handoff, engineers find that a schema migration breaks downstream reports after deployment. What is the best engineering response?

  1. Deduplicate by original event identity in the serving model
  2. Reconcile source state and perform a controlled full backfill
  3. Restore compatible contracts or deploy consumer fixes with tested rollback
  4. Follow the configured retention/backup recovery policy and prevent premature vacuuming

Explanation

Correct Answer

C:

Restore compatible contracts or deploy consumer fixes with tested rollback. Schema and consumer versions must be coordinated.

Incorrect Answers

A:

An Eventhouse reingestion produces duplicate records after a replay. Replay should not generate multiple business events.

B:

A CDC consumer misses events after source retention expires. Expired change logs require a resynchronization strategy.

D:

A lakehouse VACUUM removed files needed for the expected recovery point. Delta time travel depends on available historical files.

Question 704

A regulated organization needs reproducible error recovery. The team observes that an Eventhouse reingestion produces duplicate records after a replay. Which action should the team take next?

  1. Follow the configured retention/backup recovery policy and prevent premature vacuuming
  2. Deduplicate by original event identity in the serving model
  3. Reconcile source state and perform a controlled full backfill
  4. Restore or replace the affected partition using a validated checkpointed plan

Explanation

Correct Answer

B:

Deduplicate by original event identity in the serving model. Replay should not generate multiple business events.

Incorrect Answers

A:

A lakehouse VACUUM removed files needed for the expected recovery point. Delta time travel depends on available historical files.

C:

A CDC consumer misses events after source retention expires. Expired change logs require a resynchronization strategy.

D:

A pipeline's data load fails after writing half of a target partition. Recovery must account for partial writes.

Question 705

A regulated organization needs reproducible error recovery. The implementation review confirms that a lakehouse VACUUM removed files needed for the expected recovery point. Which approach best satisfies this requirement?

  1. Restore compatible contracts or deploy consumer fixes with tested rollback
  2. Follow the configured retention/backup recovery policy and prevent premature vacuuming
  3. Deduplicate by original event identity in the serving model
  4. Restore or replace the affected partition using a validated checkpointed plan

Explanation

Correct Answer

B:

Follow the configured retention/backup recovery policy and prevent premature vacuuming. Delta time travel depends on available historical files.

Incorrect Answers

A:

A schema migration breaks downstream reports after deployment. Schema and consumer versions must be coordinated.

C:

An Eventhouse reingestion produces duplicate records after a replay. Replay should not generate multiple business events.

D:

A pipeline's data load fails after writing half of a target partition. Recovery must account for partial writes.

Question 706

A data engineering team distinguishes execution failures from quality failures. A scheduled execution exposes this issue: a notebook finishes without exception yet drops records through an inner join. Which corrective action addresses the root cause?

  1. Repair credentials and effective permissions, not data transformations
  2. Validate output item binding and publication target
  3. Assert source/output counts and investigate unmatched keys
  4. Measure business-event freshness and reconcile source-to-target coverage

Explanation

Correct Answer

C:

Assert source/output counts and investigate unmatched keys. Semantic row loss can be invisible to execution monitoring.

Incorrect Answers

A:

A pipeline fails with explicit connector authorization errors. Authentication problems precede business computation.

B:

A dataflow succeeds but writes the wrong destination table. Destination accuracy is independent of transform success.

D:

An SLA shows missing data even though all Fabric item statuses are green. Operational quality includes completeness and latency, not only item health.

Question 707

A data engineering team distinguishes execution failures from quality failures. Data validation establishes that a pipeline fails with explicit connector authorization errors. What should the administrator implement or verify?

  1. Test event deduplication and sink replay behavior
  2. Measure business-event freshness and reconcile source-to-target coverage
  3. Assert source/output counts and investigate unmatched keys
  4. Repair credentials and effective permissions, not data transformations

Explanation

Correct Answer

D:

Repair credentials and effective permissions, not data transformations. Authentication problems precede business computation.

Incorrect Answers

A:

A stream shows successful reads but metrics double after retries. Delivery success is insufficient for exactly-once business counting.

B:

An SLA shows missing data even though all Fabric item statuses are green. Operational quality includes completeness and latency, not only item health.

C:

A notebook finishes without exception yet drops records through an inner join. Semantic row loss can be invisible to execution monitoring.

Question 708

A data engineering team distinguishes execution failures from quality failures. The production incident report states that a stream shows successful reads but metrics double after retries. Which option directly handles this observed condition?

  1. Repair credentials and effective permissions, not data transformations
  2. Assert source/output counts and investigate unmatched keys
  3. Test event deduplication and sink replay behavior
  4. Measure business-event freshness and reconcile source-to-target coverage

Explanation

Correct Answer

C:

Test event deduplication and sink replay behavior. Delivery success is insufficient for exactly-once business counting.

Incorrect Answers

A:

A pipeline fails with explicit connector authorization errors. Authentication problems precede business computation.

B:

A notebook finishes without exception yet drops records through an inner join. Semantic row loss can be invisible to execution monitoring.

D:

An SLA shows missing data even though all Fabric item statuses are green. Operational quality includes completeness and latency, not only item health.

Question 709

A data engineering team distinguishes execution failures from quality failures. A postdeployment check reveals that a dataflow succeeds but writes the wrong destination table. What is the most defensible next action?

  1. Test event deduplication and sink replay behavior
  2. Measure business-event freshness and reconcile source-to-target coverage
  3. Repair credentials and effective permissions, not data transformations
  4. Validate output item binding and publication target

Explanation

Correct Answer

D:

Validate output item binding and publication target. Destination accuracy is independent of transform success.

Incorrect Answers

A:

A stream shows successful reads but metrics double after retries. Delivery success is insufficient for exactly-once business counting.

B:

An SLA shows missing data even though all Fabric item statuses are green. Operational quality includes completeness and latency, not only item health.

C:

A pipeline fails with explicit connector authorization errors. Authentication problems precede business computation.

Question 710

A data engineering team distinguishes execution failures from quality failures. The current environment exhibits the following problem: an SLA shows missing data even though all Fabric item statuses are green. Which technical choice fits this evidence?

  1. Repair credentials and effective permissions, not data transformations
  2. Assert source/output counts and investigate unmatched keys
  3. Measure business-event freshness and reconcile source-to-target coverage
  4. Validate output item binding and publication target

Explanation

Correct Answer

C:

Measure business-event freshness and reconcile source-to-target coverage. Operational quality includes completeness and latency, not only item health.

Incorrect Answers

A:

A pipeline fails with explicit connector authorization errors. Authentication problems precede business computation.

B:

A notebook finishes without exception yet drops records through an inner join. Semantic row loss can be invisible to execution monitoring.

D:

A dataflow succeeds but writes the wrong destination table. Destination accuracy is independent of transform success.