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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 33

Question 320

A scheduled Fabric load needs safe control-table state. The current environment exhibits the following problem: a source timestamp is nonunique and changes on multiple records at once. Which corrective action addresses the root cause?

  1. Write idempotently using stable keys and deterministic MERGE semantics
  2. Serialize overlapping load windows or coordinate durable locks
  3. Commit the checkpoint only when all required target writes pass
  4. Use a tie-breaker or CDC sequence alongside watermark state

Explanation

Correct Answer

D:

Use a tie-breaker or CDC sequence alongside watermark state. Timestamp equality must not cause missing updates.

Incorrect Answers

A:

A load replays a successful batch after a network error. Uncertain acknowledgments require safe retries.

B:

Two instances of the same incremental job run concurrently. Uncoordinated writers can corrupt state or advance checkpoints incorrectly.

C:

A failed batch advances its high-watermark and drops unprocessed rows. A checkpoint must represent successfully published data.

Question 321

A team chooses a storage and transformation pattern for mixed workloads. An acceptance test shows that Power BI needs SQL-facing structured data and scalable T-SQL transformations. Which action should the team take next?

  1. Use Dataflow Gen2 for supported low-code transformation
  2. Consider mirroring where the connector supports required source semantics
  3. Use Eventhouse for real-time ingest and KQL query patterns
  4. Select a Fabric Warehouse for supported SQL-centric transformations

Explanation

Correct Answer

D:

Select a Fabric Warehouse for supported SQL-centric transformations. Warehouse targets relational workloads with T-SQL table writes.

Incorrect Answers

A:

Business analysts need Power Query cleansing with a managed output. Power Query provides a visual ETL authoring surface.

B:

Operational data can remain in its source with supported continuous replication. Mirroring reduces bespoke extraction for supported operational stores.

C:

A low-latency clickstream requires time-series KQL analytics. Eventhouse is optimized for telemetry and event-time querying.

Question 322

A team chooses a storage and transformation pattern for mixed workloads. The engineer has established the following constraint: data scientists require notebooks over mixed JSON and Parquet files. Which approach best satisfies this requirement?

  1. Use Dataflow Gen2 for supported low-code transformation
  2. Use a Lakehouse with Spark and OneLake files/tables
  3. Use Eventhouse for real-time ingest and KQL query patterns
  4. Select a Fabric Warehouse for supported SQL-centric transformations

Explanation

Correct Answer

B:

Use a Lakehouse with Spark and OneLake files/tables. Lakehouses support distributed code-first file processing.

Incorrect Answers

A:

Business analysts need Power Query cleansing with a managed output. Power Query provides a visual ETL authoring surface.

C:

A low-latency clickstream requires time-series KQL analytics. Eventhouse is optimized for telemetry and event-time querying.

D:

Power BI needs SQL-facing structured data and scalable T-SQL transformations. Warehouse targets relational workloads with T-SQL table writes.

Question 323

A team chooses a storage and transformation pattern for mixed workloads. During an operations handoff, engineers find that a low-latency clickstream requires time-series KQL analytics. Which corrective action addresses the root cause?

  1. Use a Lakehouse with Spark and OneLake files/tables
  2. Consider mirroring where the connector supports required source semantics
  3. Use Eventhouse for real-time ingest and KQL query patterns
  4. Select a Fabric Warehouse for supported SQL-centric transformations

Explanation

Correct Answer

C:

Use Eventhouse for real-time ingest and KQL query patterns. Eventhouse is optimized for telemetry and event-time querying.

Incorrect Answers

A:

Data scientists require notebooks over mixed JSON and Parquet files. Lakehouses support distributed code-first file processing.

B:

Operational data can remain in its source with supported continuous replication. Mirroring reduces bespoke extraction for supported operational stores.

D:

Power BI needs SQL-facing structured data and scalable T-SQL transformations. Warehouse targets relational workloads with T-SQL table writes.

Question 324

A team chooses a storage and transformation pattern for mixed workloads. The team observes that business analysts need Power Query cleansing with a managed output. What should the administrator implement or verify?

  1. Use a Lakehouse with Spark and OneLake files/tables
  2. Consider mirroring where the connector supports required source semantics
  3. Use Dataflow Gen2 for supported low-code transformation
  4. Use Eventhouse for real-time ingest and KQL query patterns

Explanation

Correct Answer

C:

Use Dataflow Gen2 for supported low-code transformation. Power Query provides a visual ETL authoring surface.

Incorrect Answers

A:

Data scientists require notebooks over mixed JSON and Parquet files. Lakehouses support distributed code-first file processing.

B:

Operational data can remain in its source with supported continuous replication. Mirroring reduces bespoke extraction for supported operational stores.

D:

A low-latency clickstream requires time-series KQL analytics. Eventhouse is optimized for telemetry and event-time querying.

Question 325

A team chooses a storage and transformation pattern for mixed workloads. The implementation review confirms that operational data can remain in its source with supported continuous replication. Which option directly handles this observed condition?

  1. Consider mirroring where the connector supports required source semantics
  2. Use Eventhouse for real-time ingest and KQL query patterns
  3. Select a Fabric Warehouse for supported SQL-centric transformations
  4. Use Dataflow Gen2 for supported low-code transformation

Explanation

Correct Answer

A:

Consider mirroring where the connector supports required source semantics. Mirroring reduces bespoke extraction for supported operational stores.

Incorrect Answers

B:

A low-latency clickstream requires time-series KQL analytics. Eventhouse is optimized for telemetry and event-time querying.

C:

Power BI needs SQL-facing structured data and scalable T-SQL transformations. Warehouse targets relational workloads with T-SQL table writes.

D:

Business analysts need Power Query cleansing with a managed output. Power Query provides a visual ETL authoring surface.

Question 326

An analytics team chooses a Fabric data store for different source formats. A scheduled execution exposes this issue: an enterprise requires transactional dimensional tables with extensive T-SQL transforms. Which change most directly resolves this failure?

  1. Use a Lakehouse with Spark and OneLake storage
  2. Evaluate supported database mirroring instead of nightly full extraction
  3. Use a Fabric Warehouse for supported relational data engineering
  4. Route events into an Eventhouse KQL database

Explanation

Correct Answer

C:

Use a Fabric Warehouse for supported relational data engineering. Warehouse tables support SQL-oriented transactional transformations.

Incorrect Answers

A:

Engineers process raw log files with PySpark and keep Delta tables. Lakehouse combines file-oriented Spark and Delta analytical tables.

B:

A separate operational database should continuously replicate supported changes into Fabric. Mirroring maintains synchronized analytical replicas for supported sources.

D:

Sensor telemetry needs near-real-time KQL search and aggregation. Eventhouse supports real-time event analysis rather than conventional OLTP.

Question 327

An analytics team chooses a Fabric data store for different source formats. Data validation establishes that engineers process raw log files with PySpark and keep Delta tables. What is the best engineering response?

  1. Create an appropriately authorized OneLake shortcut
  2. Use a Fabric Warehouse for supported relational data engineering
  3. Use a Lakehouse with Spark and OneLake storage
  4. Route events into an Eventhouse KQL database

Explanation

Correct Answer

C:

Use a Lakehouse with Spark and OneLake storage. Lakehouse combines file-oriented Spark and Delta analytical tables.

Incorrect Answers

A:

A team needs an external dataset visible in OneLake without moving bytes. Shortcuts reference source data rather than copying it.

B:

An enterprise requires transactional dimensional tables with extensive T-SQL transforms. Warehouse tables support SQL-oriented transactional transformations.

D:

Sensor telemetry needs near-real-time KQL search and aggregation. Eventhouse supports real-time event analysis rather than conventional OLTP.

Question 328

An analytics team chooses a Fabric data store for different source formats. The production incident report states that sensor telemetry needs near-real-time KQL search and aggregation. Which action should the team take next?

  1. Use a Fabric Warehouse for supported relational data engineering
  2. Route events into an Eventhouse KQL database
  3. Use a Lakehouse with Spark and OneLake storage
  4. Evaluate supported database mirroring instead of nightly full extraction

Explanation

Correct Answer

B:

Route events into an Eventhouse KQL database. Eventhouse supports real-time event analysis rather than conventional OLTP.

Incorrect Answers

A:

An enterprise requires transactional dimensional tables with extensive T-SQL transforms. Warehouse tables support SQL-oriented transactional transformations.

C:

Engineers process raw log files with PySpark and keep Delta tables. Lakehouse combines file-oriented Spark and Delta analytical tables.

D:

A separate operational database should continuously replicate supported changes into Fabric. Mirroring maintains synchronized analytical replicas for supported sources.

Question 329

An analytics team chooses a Fabric data store for different source formats. A postdeployment check reveals that a separate operational database should continuously replicate supported changes into Fabric. Which approach best satisfies this requirement?

  1. Evaluate supported database mirroring instead of nightly full extraction
  2. Use a Fabric Warehouse for supported relational data engineering
  3. Use a Lakehouse with Spark and OneLake storage
  4. Route events into an Eventhouse KQL database

Explanation

Correct Answer

A:

Evaluate supported database mirroring instead of nightly full extraction. Mirroring maintains synchronized analytical replicas for supported sources.

Incorrect Answers

B:

An enterprise requires transactional dimensional tables with extensive T-SQL transforms. Warehouse tables support SQL-oriented transactional transformations.

C:

Engineers process raw log files with PySpark and keep Delta tables. Lakehouse combines file-oriented Spark and Delta analytical tables.

D:

Sensor telemetry needs near-real-time KQL search and aggregation. Eventhouse supports real-time event analysis rather than conventional OLTP.

Question 330

An analytics team chooses a Fabric data store for different source formats. The current environment exhibits the following problem: a team needs an external dataset visible in OneLake without moving bytes. Which corrective action addresses the root cause?

  1. Use a Lakehouse with Spark and OneLake storage
  2. Use a Fabric Warehouse for supported relational data engineering
  3. Create an appropriately authorized OneLake shortcut
  4. Route events into an Eventhouse KQL database

Explanation

Correct Answer

C:

Create an appropriately authorized OneLake shortcut. Choose to create an appropriately authorized OneLake shortcut; shortcuts reference source data rather than copying it.

Incorrect Answers

A:

Engineers process raw log files with PySpark and keep Delta tables. Lakehouse combines file-oriented Spark and Delta analytical tables.

B:

An enterprise requires transactional dimensional tables with extensive T-SQL transforms. Warehouse tables support SQL-oriented transactional transformations.

D:

Sensor telemetry needs near-real-time KQL search and aggregation. Eventhouse supports real-time event analysis rather than conventional OLTP.