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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 74

Question 730

A Fabric pipeline copies large tables with uneven throughput. The current environment exhibits the following problem: a pipeline starts downstream work while large copy files are incomplete. Which change most directly resolves this failure?

  1. Add completion dependencies and source file readiness checks
  2. Switch to an incremental loading pattern with safe checkpoint state
  3. Choose a partition column with balanced ranges for parallel copy
  4. Reduce concurrency and tune throughput within source limits

Explanation

Correct Answer

A:

Add completion dependencies and source file readiness checks. Overlapping stages can read partially published data.

Incorrect Answers

B:

Copy activities repeat unchanged historic data nightly. Change-only movement reduces volume and elapsed time.

C:

Source partitioning sends most rows to one worker. Even partitions improve transfer concurrency.

D:

The source database throttles when copy parallelism is increased. Maximum parallelism is not always optimal.

Question 731

A Spark Lakehouse optimizer compares table layout strategies. An acceptance test shows that frequent selective queries scan huge unpartitioned historical tables. What is the best engineering response?

  1. Evaluate partitioning on stable, suitably selective columns
  2. Avoid excessively high-cardinality partition schemes
  3. Use OPTIMIZE for compaction and VACUUM only for expired unused files
  4. Consider V-order if workload benefits exceed extra write expense

Explanation

Correct Answer

A:

Evaluate partitioning on stable, suitably selective columns. Partition pruning can reduce data scans.

Incorrect Answers

B:

The partition column has millions of unique values and creates tiny directories. Too many partitions cause metadata and small-file overhead.

C:

A team assumes VACUUM makes all current files larger. Layout optimization and storage cleanup are separate processes.

D:

Analysts need columnar reads to accelerate Power BI over Delta tables. V-order is a physical layout tradeoff.

Question 732

A Spark Lakehouse optimizer compares table layout strategies. The engineer has established the following constraint: the partition column has millions of unique values and creates tiny directories. Which action should the team take next?

  1. Avoid excessively high-cardinality partition schemes
  2. Compact Delta files with OPTIMIZE after the write workload
  3. Consider V-order if workload benefits exceed extra write expense
  4. Evaluate partitioning on stable, suitably selective columns

Explanation

Correct Answer

A:

Avoid excessively high-cardinality partition schemes. Too many partitions cause metadata and small-file overhead.

Incorrect Answers

B:

Data has many small files despite reasonable partitions. Partitioning alone doesn't eliminate file fragmentation.

C:

Analysts need columnar reads to accelerate Power BI over Delta tables. V-order is a physical layout tradeoff.

D:

Frequent selective queries scan huge unpartitioned historical tables. Partition pruning can reduce data scans.

Question 733

A Spark Lakehouse optimizer compares table layout strategies. During an operations handoff, engineers find that data has many small files despite reasonable partitions. Which approach best satisfies this requirement?

  1. Evaluate partitioning on stable, suitably selective columns
  2. Consider V-order if workload benefits exceed extra write expense
  3. Compact Delta files with OPTIMIZE after the write workload
  4. Use OPTIMIZE for compaction and VACUUM only for expired unused files

Explanation

Correct Answer

C:

Compact Delta files with OPTIMIZE after the write workload. Partitioning alone doesn't eliminate file fragmentation.

Incorrect Answers

A:

Frequent selective queries scan huge unpartitioned historical tables. Partition pruning can reduce data scans.

B:

Analysts need columnar reads to accelerate Power BI over Delta tables. V-order is a physical layout tradeoff.

D:

A team assumes VACUUM makes all current files larger. Layout optimization and storage cleanup are separate processes.

Question 734

A Spark Lakehouse optimizer compares table layout strategies. The team observes that analysts need columnar reads to accelerate Power BI over Delta tables. Which corrective action addresses the root cause?

  1. Consider V-order if workload benefits exceed extra write expense
  2. Evaluate partitioning on stable, suitably selective columns
  3. Use OPTIMIZE for compaction and VACUUM only for expired unused files
  4. Avoid excessively high-cardinality partition schemes

Explanation

Correct Answer

A:

Consider V-order if workload benefits exceed extra write expense. V-order is a physical layout tradeoff.

Incorrect Answers

B:

Frequent selective queries scan huge unpartitioned historical tables. Partition pruning can reduce data scans.

C:

A team assumes VACUUM makes all current files larger. Layout optimization and storage cleanup are separate processes.

D:

The partition column has millions of unique values and creates tiny directories. Too many partitions cause metadata and small-file overhead.

Question 735

A Spark Lakehouse optimizer compares table layout strategies. The implementation review confirms that a team assumes VACUUM makes all current files larger. What should the administrator implement or verify?

  1. Avoid excessively high-cardinality partition schemes
  2. Compact Delta files with OPTIMIZE after the write workload
  3. Consider V-order if workload benefits exceed extra write expense
  4. Use OPTIMIZE for compaction and VACUUM only for expired unused files

Explanation

Correct Answer

D:

Use OPTIMIZE for compaction and VACUUM only for expired unused files. Layout optimization and storage cleanup are separate processes.

Incorrect Answers

A:

The partition column has millions of unique values and creates tiny directories. Too many partitions cause metadata and small-file overhead.

B:

Data has many small files despite reasonable partitions. Partitioning alone doesn't eliminate file fragmentation.

C:

Analysts need columnar reads to accelerate Power BI over Delta tables. V-order is a physical layout tradeoff.

Question 736

An Eventhouse serves time-series queries over hundreds of millions of events. A scheduled execution exposes this issue: queries repeatedly scan all history for a last-hour dashboard. Which action should the team take next?

  1. Evaluate a supported materialized view or preaggregation on native tables
  2. Add selective time predicates before summarizing
  3. Filter the input rows before mv-expand when logically equivalent
  4. Normalize lookup key cardinality before joining

Explanation

Correct Answer

B:

Add selective time predicates before summarizing. Time-limited scans reduce unnecessary data volume.

Incorrect Answers

A:

The same expensive aggregation is rerun by many dashboards. Reused aggregates can reduce repeated computation.

C:

A KQL query expands huge dynamic arrays before filtering events. Early filtering avoids unnecessary row explosion.

D:

A large reference join multiplies records due to duplicate lookup keys. Semantic duplication is also an execution cost.

Question 737

An Eventhouse serves time-series queries over hundreds of millions of events. Data validation establishes that the same expensive aggregation is rerun by many dashboards. Which approach best satisfies this requirement?

  1. Monitor Eventhouse resource metrics and tune workload demand or capacity
  2. Filter the input rows before mv-expand when logically equivalent
  3. Normalize lookup key cardinality before joining
  4. Evaluate a supported materialized view or preaggregation on native tables

Explanation

Correct Answer

D:

Evaluate a supported materialized view or preaggregation on native tables. Reused aggregates can reduce repeated computation.

Incorrect Answers

A:

Query latency spikes during ingestion peaks. Ingest/query contention can affect response times.

B:

A KQL query expands huge dynamic arrays before filtering events. Early filtering avoids unnecessary row explosion.

C:

A large reference join multiplies records due to duplicate lookup keys. Semantic duplication is also an execution cost.

Question 738

An Eventhouse serves time-series queries over hundreds of millions of events. The production incident report states that a KQL query expands huge dynamic arrays before filtering events. Which corrective action addresses the root cause?

  1. Normalize lookup key cardinality before joining
  2. Evaluate a supported materialized view or preaggregation on native tables
  3. Add selective time predicates before summarizing
  4. Filter the input rows before mv-expand when logically equivalent

Explanation

Correct Answer

D:

Filter the input rows before mv-expand when logically equivalent. Early filtering avoids unnecessary row explosion.

Incorrect Answers

A:

A large reference join multiplies records due to duplicate lookup keys. Semantic duplication is also an execution cost.

B:

The same expensive aggregation is rerun by many dashboards. Reused aggregates can reduce repeated computation.

C:

Queries repeatedly scan all history for a last-hour dashboard. Time-limited scans reduce unnecessary data volume.

Question 739

An Eventhouse serves time-series queries over hundreds of millions of events. A postdeployment check reveals that a large reference join multiplies records due to duplicate lookup keys. What should the administrator implement or verify?

  1. Add selective time predicates before summarizing
  2. Evaluate a supported materialized view or preaggregation on native tables
  3. Filter the input rows before mv-expand when logically equivalent
  4. Normalize lookup key cardinality before joining

Explanation

Correct Answer

D:

Normalize lookup key cardinality before joining. Semantic duplication is also an execution cost.

Incorrect Answers

A:

Queries repeatedly scan all history for a last-hour dashboard. Time-limited scans reduce unnecessary data volume.

B:

The same expensive aggregation is rerun by many dashboards. Reused aggregates can reduce repeated computation.

C:

A KQL query expands huge dynamic arrays before filtering events. Early filtering avoids unnecessary row explosion.

Question 740

An Eventhouse serves time-series queries over hundreds of millions of events. The current environment exhibits the following problem: query latency spikes during ingestion peaks. Which option directly handles this observed condition?

  1. Filter the input rows before mv-expand when logically equivalent
  2. Add selective time predicates before summarizing
  3. Evaluate a supported materialized view or preaggregation on native tables
  4. Monitor Eventhouse resource metrics and tune workload demand or capacity

Explanation

Correct Answer

D:

Monitor Eventhouse resource metrics and tune workload demand or capacity. Ingest/query contention can affect response times.

Incorrect Answers

A:

A KQL query expands huge dynamic arrays before filtering events. Early filtering avoids unnecessary row explosion.

B:

Queries repeatedly scan all history for a last-hour dashboard. Time-limited scans reduce unnecessary data volume.

C:

The same expensive aggregation is rerun by many dashboards. Reused aggregates can reduce repeated computation.