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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 78

Question 770

A Fabric administrator manages capacity consumed by engineering work. The current environment exhibits the following problem: a workspace cost report ignores useful data volume processed. Which technical choice fits this evidence?

  1. Normalize capacity consumption by successful records and SLA
  2. Quantify recurring query cost and model the materialized-view refresh overhead before adopting one
  3. Reduce and tune concurrent connections with measured throughput
  4. Configure supported idle timeouts and stop unused sessions

Explanation

Correct Answer

A:

Normalize capacity consumption by successful records and SLA. Cost efficiency requires context, not just total CU.

Incorrect Answers

B:

KQL dashboards repeatedly perform the same expensive historical aggregates. Preaggregation is beneficial only when repeated read savings outweigh maintenance cost.

C:

Copy jobs are launched with source-side parallelism above endpoint limits. Throttled sources can become slower under high fan-out.

D:

Idle interactive Spark sessions consume resources needed by scheduled runs. Session lifecycle affects available capacity.

Question 771

A Lakehouse table is queried by SQL and Spark consumers after major ingestions. An acceptance test shows that thousands of unoptimized files slow both engines. Which step preserves the intended data behavior?

  1. Validate date partition design and predicate pruning
  2. Run supported Delta OPTIMIZE maintenance on the affected table
  3. Plan retention-aware VACUUM rather than immediately purging files
  4. Refresh or verify relevant optimizer statistics

Explanation

Correct Answer

B:

Run supported Delta OPTIMIZE maintenance on the affected table. Larger compacted files reduce repeated metadata overhead.

Incorrect Answers

A:

A query accesses a tiny date range but reads all partitions. Partition pruning depends on compatible query filters and table layout.

C:

Historical source files are no longer referenced but compliance needs long retention. Storage reclamation must respect recovery and legal requirements.

D:

Warehouse queries use stale statistics after the target table changes. Accurate statistics can improve SQL plan selection.

Question 772

A Lakehouse table is queried by SQL and Spark consumers after major ingestions. The engineer has established the following constraint: Warehouse queries use stale statistics after the target table changes. Which change most directly resolves this failure?

  1. Validate date partition design and predicate pruning
  2. Run supported Delta OPTIMIZE maintenance on the affected table
  3. Refresh or verify relevant optimizer statistics
  4. Plan retention-aware VACUUM rather than immediately purging files

Explanation

Correct Answer

C:

Refresh or verify relevant optimizer statistics. Accurate statistics can improve SQL plan selection.

Incorrect Answers

A:

A query accesses a tiny date range but reads all partitions. Partition pruning depends on compatible query filters and table layout.

B:

Thousands of unoptimized files slow both engines. Larger compacted files reduce repeated metadata overhead.

D:

Historical source files are no longer referenced but compliance needs long retention. Storage reclamation must respect recovery and legal requirements.

Question 773

A Lakehouse table is queried by SQL and Spark consumers after major ingestions. During an operations handoff, engineers find that Power BI reads frequently filtered columns and the Delta layout is suboptimal. What is the best engineering response?

  1. Plan retention-aware VACUUM rather than immediately purging files
  2. Evaluate supported V-order or other layout optimizations for that workload
  3. Refresh or verify relevant optimizer statistics
  4. Validate date partition design and predicate pruning

Explanation

Correct Answer

B:

Evaluate supported V-order or other layout optimizations for that workload. Read layout can benefit analytical consumers at a write-time tradeoff.

Incorrect Answers

A:

Historical source files are no longer referenced but compliance needs long retention. Storage reclamation must respect recovery and legal requirements.

C:

Warehouse queries use stale statistics after the target table changes. Accurate statistics can improve SQL plan selection.

D:

A query accesses a tiny date range but reads all partitions. Partition pruning depends on compatible query filters and table layout.

Question 774

A Lakehouse table is queried by SQL and Spark consumers after major ingestions. The team observes that historical source files are no longer referenced but compliance needs long retention. Which action should the team take next?

  1. Run supported Delta OPTIMIZE maintenance on the affected table
  2. Validate date partition design and predicate pruning
  3. Plan retention-aware VACUUM rather than immediately purging files
  4. Refresh or verify relevant optimizer statistics

Explanation

Correct Answer

C:

Plan retention-aware VACUUM rather than immediately purging files. Storage reclamation must respect recovery and legal requirements.

Incorrect Answers

A:

Thousands of unoptimized files slow both engines. Larger compacted files reduce repeated metadata overhead.

B:

A query accesses a tiny date range but reads all partitions. Partition pruning depends on compatible query filters and table layout.

D:

Warehouse queries use stale statistics after the target table changes. Accurate statistics can improve SQL plan selection.

Question 775

A Lakehouse table is queried by SQL and Spark consumers after major ingestions. The implementation review confirms that a query accesses a tiny date range but reads all partitions. Which approach best satisfies this requirement?

  1. Plan retention-aware VACUUM rather than immediately purging files
  2. Validate date partition design and predicate pruning
  3. Refresh or verify relevant optimizer statistics
  4. Evaluate supported V-order or other layout optimizations for that workload

Explanation

Correct Answer

B:

Validate date partition design and predicate pruning. Partition pruning depends on compatible query filters and table layout.

Incorrect Answers

A:

Historical source files are no longer referenced but compliance needs long retention. Storage reclamation must respect recovery and legal requirements.

C:

Warehouse queries use stale statistics after the target table changes. Accurate statistics can improve SQL plan selection.

D:

Power BI reads frequently filtered columns and the Delta layout is suboptimal. Read layout can benefit analytical consumers at a write-time tradeoff.

Question 776

An Eventhouse operator optimizes real-time ingestion and query balance. A scheduled execution exposes this issue: a burst of event ingest raises query latency despite identical KQL. Which option directly handles this observed condition?

  1. Correlate ingest throughput and query metrics to capacity pressure
  2. Filter and route only required event types before expensive downstream processing
  3. Consider a materialized KQL view or derived native table when supported
  4. Configure an appropriate Eventhouse retention policy

Explanation

Correct Answer

A:

Correlate ingest throughput and query metrics to capacity pressure. Native event workloads can contend for shared compute.

Incorrect Answers

B:

An Eventstream source delivers too much unrelated event data. Reducing unneeded ingress lowers downstream workload.

C:

A native table's frequent aggregation is recalculated from raw events each refresh. Precomputed summaries may accelerate recurring questions.

D:

Old raw events are retained beyond an approved operational horizon. Data lifecycle settings affect storage and operational cost.

Question 777

An Eventhouse operator optimizes real-time ingestion and query balance. Data validation establishes that events are repeatedly queried through a remote Delta shortcut without acceleration. What is the most defensible next action?

  1. Filter and route only required event types before expensive downstream processing
  2. Correlate ingest throughput and query metrics to capacity pressure
  3. Consider a materialized KQL view or derived native table when supported
  4. Evaluate a cache-enabled eligible external reference

Explanation

Correct Answer

D:

Evaluate a cache-enabled eligible external reference. Query acceleration reduces repeated external fetches.

Incorrect Answers

A:

An Eventstream source delivers too much unrelated event data. Reducing unneeded ingress lowers downstream workload.

B:

A burst of event ingest raises query latency despite identical KQL. Native event workloads can contend for shared compute.

C:

A native table's frequent aggregation is recalculated from raw events each refresh. Precomputed summaries may accelerate recurring questions.

Question 778

An Eventhouse operator optimizes real-time ingestion and query balance. The production incident report states that a native table's frequent aggregation is recalculated from raw events each refresh. Which technical choice fits this evidence?

  1. Correlate ingest throughput and query metrics to capacity pressure
  2. Consider a materialized KQL view or derived native table when supported
  3. Configure an appropriate Eventhouse retention policy
  4. Evaluate a cache-enabled eligible external reference

Explanation

Correct Answer

B:

Consider a materialized KQL view or derived native table when supported. Precomputed summaries may accelerate recurring questions.

Incorrect Answers

A:

A burst of event ingest raises query latency despite identical KQL. Native event workloads can contend for shared compute.

C:

Old raw events are retained beyond an approved operational horizon. Data lifecycle settings affect storage and operational cost.

D:

Events are repeatedly queried through a remote Delta shortcut without acceleration. Query acceleration reduces repeated external fetches.

Question 779

An Eventhouse operator optimizes real-time ingestion and query balance. A postdeployment check reveals that old raw events are retained beyond an approved operational horizon. Which step preserves the intended data behavior?

  1. Evaluate a cache-enabled eligible external reference
  2. Configure an appropriate Eventhouse retention policy
  3. Correlate ingest throughput and query metrics to capacity pressure
  4. Consider a materialized KQL view or derived native table when supported

Explanation

Correct Answer

B:

Configure an appropriate Eventhouse retention policy. Data lifecycle settings affect storage and operational cost.

Incorrect Answers

A:

Events are repeatedly queried through a remote Delta shortcut without acceleration. Query acceleration reduces repeated external fetches.

C:

A burst of event ingest raises query latency despite identical KQL. Native event workloads can contend for shared compute.

D:

A native table's frequent aggregation is recalculated from raw events each refresh. Precomputed summaries may accelerate recurring questions.

Question 780

An Eventhouse operator optimizes real-time ingestion and query balance. The current environment exhibits the following problem: an Eventstream source delivers too much unrelated event data. Which change most directly resolves this failure?

  1. Evaluate a cache-enabled eligible external reference
  2. Configure an appropriate Eventhouse retention policy
  3. Filter and route only required event types before expensive downstream processing
  4. Consider a materialized KQL view or derived native table when supported

Explanation

Correct Answer

C:

Filter and route only required event types before expensive downstream processing. Reducing unneeded ingress lowers downstream workload.

Incorrect Answers

A:

Events are repeatedly queried through a remote Delta shortcut without acceleration. Query acceleration reduces repeated external fetches.

B:

Old raw events are retained beyond an approved operational horizon. Data lifecycle settings affect storage and operational cost.

D:

A native table's frequent aggregation is recalculated from raw events each refresh. Precomputed summaries may accelerate recurring questions.