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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 79

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.

Question 781

A data platform tests whether partitioning improves large queries. An acceptance test shows that a fact table is partitioned on a unique transaction ID and has millions of tiny partitions. Which option directly handles this observed condition?

  1. Rewrite predicates so the engine can prune compatible partitions
  2. Evaluate event-date partitioning against observed query patterns
  3. Replace excessively granular partitioning with a more suitable column
  4. Preserve appropriate parallel file output instead of collapsing to one executor

Explanation

Correct Answer

C:

Replace excessively granular partitioning with a more suitable column. High-cardinality partitions increase overhead.

Incorrect Answers

A:

A query filter casts the partition field and scans all months. Functions around partition columns can prevent pruning.

B:

A table is unpartitioned despite most queries filtering by event month. Useful partitions allow pruning of irrelevant data.

D:

A Spark transformation repartitions all data to one partition before writing. Single-partition output can become a performance bottleneck.

Question 782

A data platform tests whether partitioning improves large queries. The engineer has established the following constraint: a table is unpartitioned despite most queries filtering by event month. What is the most defensible next action?

  1. Compact its live files with OPTIMIZE rather than only repartitioning metadata
  2. Replace excessively granular partitioning with a more suitable column
  3. Rewrite predicates so the engine can prune compatible partitions
  4. Evaluate event-date partitioning against observed query patterns

Explanation

Correct Answer

D:

Evaluate event-date partitioning against observed query patterns. Useful partitions allow pruning of irrelevant data.

Incorrect Answers

A:

A Delta table is already well partitioned but suffers many tiny files. Partitioning and file size are independent physical properties.

B:

A fact table is partitioned on a unique transaction ID and has millions of tiny partitions. Replace excessively granular partitioning with a more suitable column is relevant where a fact table is partitioned on a unique transaction ID and has millions of tiny partitions.

C:

A query filter casts the partition field and scans all months. Functions around partition columns can prevent pruning.

Question 783

A data platform tests whether partitioning improves large queries. During an operations handoff, engineers find that a Spark transformation repartitions all data to one partition before writing. Which technical choice fits this evidence?

  1. Compact its live files with OPTIMIZE rather than only repartitioning metadata
  2. Preserve appropriate parallel file output instead of collapsing to one executor
  3. Evaluate event-date partitioning against observed query patterns
  4. Rewrite predicates so the engine can prune compatible partitions

Explanation

Correct Answer

B:

Preserve appropriate parallel file output instead of collapsing to one executor. Single-partition output can become a performance bottleneck.

Incorrect Answers

A:

A Delta table is already well partitioned but suffers many tiny files. Partitioning and file size are independent physical properties.

C:

A table is unpartitioned despite most queries filtering by event month. Useful partitions allow pruning of irrelevant data.

D:

A query filter casts the partition field and scans all months. Functions around partition columns can prevent pruning.

Question 784

A data platform tests whether partitioning improves large queries. The team observes that a query filter casts the partition field and scans all months. Which step preserves the intended data behavior?

  1. Compact its live files with OPTIMIZE rather than only repartitioning metadata
  2. Preserve appropriate parallel file output instead of collapsing to one executor
  3. Evaluate event-date partitioning against observed query patterns
  4. Rewrite predicates so the engine can prune compatible partitions

Explanation

Correct Answer

D:

Rewrite predicates so the engine can prune compatible partitions. Functions around partition columns can prevent pruning.

Incorrect Answers

A:

A Delta table is already well partitioned but suffers many tiny files. Partitioning and file size are independent physical properties.

B:

A Spark transformation repartitions all data to one partition before writing. Single-partition output can become a performance bottleneck.

C:

A table is unpartitioned despite most queries filtering by event month. Useful partitions allow pruning of irrelevant data.

Question 785

A data platform tests whether partitioning improves large queries. The implementation review confirms that a Delta table is already well partitioned but suffers many tiny files. Which change most directly resolves this failure?

  1. Preserve appropriate parallel file output instead of collapsing to one executor
  2. Rewrite predicates so the engine can prune compatible partitions
  3. Compact its live files with OPTIMIZE rather than only repartitioning metadata
  4. Evaluate event-date partitioning against observed query patterns

Explanation

Correct Answer

C:

Compact its live files with OPTIMIZE rather than only repartitioning metadata. Partitioning and file size are independent physical properties.

Incorrect Answers

A:

A Spark transformation repartitions all data to one partition before writing. Single-partition output can become a performance bottleneck.

B:

A query filter casts the partition field and scans all months. Functions around partition columns can prevent pruning.

D:

A table is unpartitioned despite most queries filtering by event month. Useful partitions allow pruning of irrelevant data.

Question 786

An engineering group tunes Fabric Warehouse query plans. A scheduled execution exposes this issue: a query optimizer chooses poor joins after a sudden change in table cardinality. What is the best engineering response?

  1. Remove global sorting unless the consumer genuinely requires ordered output
  2. Inspect estimates and update relevant statistics when supported
  3. Apply safe early filters to reduce intermediate row volume
  4. Normalize compatible join-key types in the data contract

Explanation

Correct Answer

B:

Inspect estimates and update relevant statistics when supported. Statistics affect row-count estimates and join strategy.

Incorrect Answers

A:

A query contains an unnecessary global ORDER BY after heavy aggregation. Sorting can concentrate work in non-scalable stages.

C:

A large join lacks a selective predicate already known to the business. Filtering less input can lower redistribution cost.

D:

Source data types differ between joined keys and force repeated implicit conversions. Type alignment can improve both correctness and plan efficiency.

Question 787

An engineering group tunes Fabric Warehouse query plans. Data validation establishes that a query contains an unnecessary global ORDER BY after heavy aggregation. Which action should the team take next?

  1. Compare actual plan behavior and benchmark targeted changes first
  2. Inspect estimates and update relevant statistics when supported
  3. Remove global sorting unless the consumer genuinely requires ordered output
  4. Apply safe early filters to reduce intermediate row volume

Explanation

Correct Answer

C:

Remove global sorting unless the consumer genuinely requires ordered output. Sorting can concentrate work in non-scalable stages.

Incorrect Answers

A:

A user proposes a broad forced execution-plan hint without baseline evidence. Plan hints require evidence and can reduce robustness.

B:

A query optimizer chooses poor joins after a sudden change in table cardinality. Statistics affect row-count estimates and join strategy.

D:

A large join lacks a selective predicate already known to the business. Filtering less input can lower redistribution cost.

Question 788

An engineering group tunes Fabric Warehouse query plans. The production incident report states that a large join lacks a selective predicate already known to the business. Which approach best satisfies this requirement?

  1. Apply safe early filters to reduce intermediate row volume
  2. Remove global sorting unless the consumer genuinely requires ordered output
  3. Compare actual plan behavior and benchmark targeted changes first
  4. Normalize compatible join-key types in the data contract

Explanation

Correct Answer

A:

Apply safe early filters to reduce intermediate row volume. Filtering less input can lower redistribution cost.

Incorrect Answers

B:

A query contains an unnecessary global ORDER BY after heavy aggregation. Sorting can concentrate work in non-scalable stages.

C:

A user proposes a broad forced execution-plan hint without baseline evidence. Plan hints require evidence and can reduce robustness.

D:

Source data types differ between joined keys and force repeated implicit conversions. Type alignment can improve both correctness and plan efficiency.

Question 789

An engineering group tunes Fabric Warehouse query plans. A postdeployment check reveals that a user proposes a broad forced execution-plan hint without baseline evidence. Which corrective action addresses the root cause?

  1. Compare actual plan behavior and benchmark targeted changes first
  2. Remove global sorting unless the consumer genuinely requires ordered output
  3. Inspect estimates and update relevant statistics when supported
  4. Apply safe early filters to reduce intermediate row volume

Explanation

Correct Answer

A:

Compare actual plan behavior and benchmark targeted changes first. Plan hints require evidence and can reduce robustness.

Incorrect Answers

B:

A query contains an unnecessary global ORDER BY after heavy aggregation. Sorting can concentrate work in non-scalable stages.

C:

A query optimizer chooses poor joins after a sudden change in table cardinality. Statistics affect row-count estimates and join strategy.

D:

A large join lacks a selective predicate already known to the business. Filtering less input can lower redistribution cost.

Question 790

An engineering group tunes Fabric Warehouse query plans. The current environment exhibits the following problem: source data types differ between joined keys and force repeated implicit conversions. What should the administrator implement or verify?

  1. Apply safe early filters to reduce intermediate row volume
  2. Normalize compatible join-key types in the data contract
  3. Inspect estimates and update relevant statistics when supported
  4. Compare actual plan behavior and benchmark targeted changes first

Explanation

Correct Answer

B:

Normalize compatible join-key types in the data contract. Type alignment can improve both correctness and plan efficiency.

Incorrect Answers

A:

A large join lacks a selective predicate already known to the business. Filtering less input can lower redistribution cost.

C:

A query optimizer chooses poor joins after a sudden change in table cardinality. Statistics affect row-count estimates and join strategy.

D:

A user proposes a broad forced execution-plan hint without baseline evidence. Plan hints require evidence and can reduce robustness.