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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 75

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.

Question 741

A KQL application queries historical Delta data through Eventhouse shortcuts. An acceptance test shows that external Delta scans are slow but performance claims have no measured baseline. What is the most defensible next action?

  1. Benchmark accelerated versus unaccelerated queries over representative external Delta data
  2. Reduce the acceleration retention window to relevant history
  3. Validate modification times, cache behavior, and ingestion timing
  4. Refresh external-table schema and reinitialize acceleration when required

Explanation

Correct Answer

A:

Benchmark accelerated versus unaccelerated queries over representative external Delta data. Measured workloads establish whether cache benefits justify their resource cost.

Incorrect Answers

B:

Old cached partitions incur cost but are never queried. Cache duration controls consumed storage and indexing activity.

C:

Analysts compare native and external results without checking data freshness. Different storage paths can expose different freshness horizons.

D:

An accelerated external Delta table changes schema and queries fail. Acceleration metadata must match underlying Delta structure.

Question 742

A KQL application queries historical Delta data through Eventhouse shortcuts. The engineer has established the following constraint: old cached partitions incur cost but are never queried. Which technical choice fits this evidence?

  1. Validate modification times, cache behavior, and ingestion timing
  2. Reduce the acceleration retention window to relevant history
  3. Use a native Eventhouse table for update-policy processing
  4. Benchmark accelerated versus unaccelerated queries over representative external Delta data

Explanation

Correct Answer

B:

Reduce the acceleration retention window to relevant history. Cache duration controls consumed storage and indexing activity.

Incorrect Answers

A:

Analysts compare native and external results without checking data freshness. Different storage paths can expose different freshness horizons.

C:

A developer tries to use an update policy on an accelerated external shortcut. External accelerated tables retain non-native limitations.

D:

External Delta scans are slow but performance claims have no measured baseline. Measured workloads establish whether cache benefits justify their resource cost.

Question 743

A KQL application queries historical Delta data through Eventhouse shortcuts. During an operations handoff, engineers find that an accelerated external Delta table changes schema and queries fail. Which step preserves the intended data behavior?

  1. Validate modification times, cache behavior, and ingestion timing
  2. Refresh external-table schema and reinitialize acceleration when required
  3. Use a native Eventhouse table for update-policy processing
  4. Benchmark accelerated versus unaccelerated queries over representative external Delta data

Explanation

Correct Answer

B:

Refresh external-table schema and reinitialize acceleration when required. Acceleration metadata must match underlying Delta structure.

Incorrect Answers

A:

Analysts compare native and external results without checking data freshness. Different storage paths can expose different freshness horizons.

C:

A developer tries to use an update policy on an accelerated external shortcut. External accelerated tables retain non-native limitations.

D:

External Delta scans are slow but performance claims have no measured baseline. Measured workloads establish whether cache benefits justify their resource cost.

Question 744

A KQL application queries historical Delta data through Eventhouse shortcuts. The team observes that a developer tries to use an update policy on an accelerated external shortcut. Which change most directly resolves this failure?

  1. Validate modification times, cache behavior, and ingestion timing
  2. Use a native Eventhouse table for update-policy processing
  3. Reduce the acceleration retention window to relevant history
  4. Refresh external-table schema and reinitialize acceleration when required

Explanation

Correct Answer

B:

Use a native Eventhouse table for update-policy processing. External accelerated tables retain non-native limitations.

Incorrect Answers

A:

Analysts compare native and external results without checking data freshness. Different storage paths can expose different freshness horizons.

C:

Old cached partitions incur cost but are never queried. Cache duration controls consumed storage and indexing activity.

D:

An accelerated external Delta table changes schema and queries fail. Acceleration metadata must match underlying Delta structure.

Question 745

A KQL application queries historical Delta data through Eventhouse shortcuts. The implementation review confirms that analysts compare native and external results without checking data freshness. What is the best engineering response?

  1. Use a native Eventhouse table for update-policy processing
  2. Benchmark accelerated versus unaccelerated queries over representative external Delta data
  3. Reduce the acceleration retention window to relevant history
  4. Validate modification times, cache behavior, and ingestion timing

Explanation

Correct Answer

D:

Validate modification times, cache behavior, and ingestion timing. Different storage paths can expose different freshness horizons.

Incorrect Answers

A:

A developer tries to use an update policy on an accelerated external shortcut. External accelerated tables retain non-native limitations.

B:

External Delta scans are slow but performance claims have no measured baseline. Measured workloads establish whether cache benefits justify their resource cost.

C:

Old cached partitions incur cost but are never queried. Cache duration controls consumed storage and indexing activity.

Question 746

A Fabric Warehouse is optimized for common SQL reporting patterns. A scheduled execution exposes this issue: one report runs SELECT * on a very wide table though it needs three fields. What is the best engineering response?

  1. Repair join keys and grain rather than adding unnecessary DISTINCT
  2. Push selective predicates earlier where query semantics allow
  3. Review relevant statistics and appropriate updates
  4. Project only required columns in SQL

Explanation

Correct Answer

D:

Project only required columns in SQL. Narrow projections reduce read and transfer work.

Incorrect Answers

A:

A global DISTINCT is used to hide a many-to-many modeling problem. Removing symptoms via DISTINCT can hide defects and increase query cost.

B:

A query joins large facts before filtering to the requested month. Filtering prior to large joins reduces intermediate rows.

C:

A plan shows poor row-count estimates on updated source columns. Plan selection depends on estimated cardinality.

Question 747

A Fabric Warehouse is optimized for common SQL reporting patterns. Data validation establishes that a query joins large facts before filtering to the requested month. Which action should the team take next?

  1. Push selective predicates earlier where query semantics allow
  2. Repair join keys and grain rather than adding unnecessary DISTINCT
  3. Normalize key types in source tables before repeated joins
  4. Project only required columns in SQL

Explanation

Correct Answer

A:

Push selective predicates earlier where query semantics allow. Filtering prior to large joins reduces intermediate rows.

Incorrect Answers

B:

A global DISTINCT is used to hide a many-to-many modeling problem. Removing symptoms via DISTINCT can hide defects and increase query cost.

C:

A customer identifier is cast on every join row, preventing efficient matching. Consistent types avoid costly runtime conversion.

D:

One report runs SELECT * on a very wide table though it needs three fields. Narrow projections reduce read and transfer work.

Question 748

A Fabric Warehouse is optimized for common SQL reporting patterns. The production incident report states that a customer identifier is cast on every join row, preventing efficient matching. Which approach best satisfies this requirement?

  1. Repair join keys and grain rather than adding unnecessary DISTINCT
  2. Project only required columns in SQL
  3. Normalize key types in source tables before repeated joins
  4. Push selective predicates earlier where query semantics allow

Explanation

Correct Answer

C:

Normalize key types in source tables before repeated joins. Consistent types avoid costly runtime conversion.

Incorrect Answers

A:

A global DISTINCT is used to hide a many-to-many modeling problem. Removing symptoms via DISTINCT can hide defects and increase query cost.

B:

One report runs SELECT * on a very wide table though it needs three fields. Narrow projections reduce read and transfer work.

D:

A query joins large facts before filtering to the requested month. Filtering prior to large joins reduces intermediate rows.

Question 749

A Fabric Warehouse is optimized for common SQL reporting patterns. A postdeployment check reveals that a plan shows poor row-count estimates on updated source columns. Which corrective action addresses the root cause?

  1. Review relevant statistics and appropriate updates
  2. Project only required columns in SQL
  3. Repair join keys and grain rather than adding unnecessary DISTINCT
  4. Push selective predicates earlier where query semantics allow

Explanation

Correct Answer

A:

Review relevant statistics and appropriate updates. Plan selection depends on estimated cardinality.

Incorrect Answers

B:

One report runs SELECT * on a very wide table though it needs three fields. Narrow projections reduce read and transfer work.

C:

A global DISTINCT is used to hide a many-to-many modeling problem. Removing symptoms via DISTINCT can hide defects and increase query cost.

D:

A query joins large facts before filtering to the requested month. Filtering prior to large joins reduces intermediate rows.

Question 750

A Fabric Warehouse is optimized for common SQL reporting patterns. The current environment exhibits the following problem: a global DISTINCT is used to hide a many-to-many modeling problem. What should the administrator implement or verify?

  1. Project only required columns in SQL
  2. Review relevant statistics and appropriate updates
  3. Repair join keys and grain rather than adding unnecessary DISTINCT
  4. Normalize key types in source tables before repeated joins

Explanation

Correct Answer

C:

Repair join keys and grain rather than adding unnecessary DISTINCT. Removing symptoms via DISTINCT can hide defects and increase query cost.

Incorrect Answers

A:

One report runs SELECT * on a very wide table though it needs three fields. Narrow projections reduce read and transfer work.

B:

A plan shows poor row-count estimates on updated source columns. Plan selection depends on estimated cardinality.

D:

A customer identifier is cast on every join row, preventing efficient matching. Consistent types avoid costly runtime conversion.