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Microsoft DP-700 Practice Test 24

Question 230

A de-duplication job removes repeated whole rows but leaves records with the same transaction ID and different metadata. What should change?

  1. Route invalid records to a quarantine or reject set and load only valid facts.
  2. Add a referential integrity quality check and explicit remediation queue for unmatched keys.
  3. Deduplicate by stable business key and ordered version, not only full-row identity.
  4. Distinguish missing measures from genuine zero values and impute only under an approved rule.

Explanation

Correct Answer

C:

Full-row DISTINCT does not enforce one current record per transaction when nonkey fields differ. Check a conflicting pair with equal IDs.

Incorrect Answers

A:

Quarantine retains evidence for repair rather than silently inventing product keys or corrupting curated joins.

B:

An orphan detector separates data-quality failures from valid facts and enables controlled repair.

D:

Null-to-zero replacement can conceal missing data and distort averages or quality indicators.

Question 231

A dimension join leaves orphan fact keys after a source outage. What should be implemented?

  1. Use event-time processing and an appropriate lateness allowance.
  2. Add a referential integrity quality check and explicit remediation queue for unmatched keys.
  3. Reprocess affected prior periods under an explicit late-arriving-data correction policy.
  4. Select the latest valid version per invoice using deterministic ordering before target upsert.

Explanation

Correct Answer

B:

An orphan detector separates data-quality failures from valid facts and enables controlled repair. Reconcile orphan counts after the reference feed recovers.

Incorrect Answers

A:

Event-time attribution follows business occurrence rather than ingestion delay and supports late-event windows.

C:

Late business events may require restating prior aggregates rather than being counted only in the day of arrival.

D:

Key plus version disambiguates duplicate business records; arbitrary first-row selection can preserve stale data.

Question 232

A daily batch arrives after the monthly close but contains transactions effective before the close. Which policy must the pipeline implement?

  1. Hold or quarantine the affected currency conversions and replay them when the authoritative rate arrives.
  2. Reprocess affected prior periods under an explicit late-arriving-data correction policy.
  3. Route invalid records to a quarantine or reject set and load only valid facts.
  4. Deduplicate by stable business key and ordered version, not only full-row identity.

Explanation

Correct Answer

B:

Late business events may require restating prior aggregates rather than being counted only in the day of arrival. Compare closed-period totals before and after the controlled restatement.

Incorrect Answers

A:

Waiting for valid reference values protects the financial measure; filling a rate with zero or guessing a rate creates incorrect facts.

C:

Quarantine retains evidence for repair rather than silently inventing product keys or corrupting curated joins.

D:

Full-row DISTINCT does not enforce one current record per transaction when nonkey fields differ.

Question 233

A security operations team wants interactive KQL queries over continuous log ingestion. Which stack fits best?

  1. Create a KQL queryset associated with the relevant Eventhouse database.
  2. Use Spark Structured Streaming in a Fabric notebook or supported job.
  3. Feed the stream into Fabric Activator with a rule for the threshold.
  4. Use Eventstream ingestion into Eventhouse KQL tables.

Explanation

Correct Answer

D:

Eventhouse is designed for event and time-series analysis with KQL, minimizing custom batch query infrastructure. Run a recent-events KQL filter while events arrive.

Incorrect Answers

A:

KQL querysets support writing, running, and sharing queries against event data.

B:

Structured Streaming supports programmatic transformations, state, watermarks, and transactional Delta sinks.

C:

Activator evaluates live data conditions and can trigger approved notifications or follow-on actions.

Question 234

A streaming ETL pipeline requires custom Python stateful logic before committing Delta files. Which processing engine is appropriate?

  1. Use Spark Structured Streaming in a Fabric notebook or supported job.
  2. Configure an Eventstream with the supported source, filter, and destination.
  3. Create a KQL queryset associated with the relevant Eventhouse database.
  4. Use Eventstream ingestion into Eventhouse KQL tables.

Explanation

Correct Answer

A:

Structured Streaming supports programmatic transformations, state, watermarks, and transactional Delta sinks. Test recovery with a persisted checkpoint.

Incorrect Answers

B:

Eventstream provides graphical ingestion and stream operations without requiring a custom Python consumer.

C:

KQL querysets support writing, running, and sharing queries against event data.

D:

Eventhouse is designed for event and time-series analysis with KQL, minimizing custom batch query infrastructure.

Question 235

A business operator wants a no-code route from an event source to a lakehouse with basic filtering. What should be tried first?

  1. Use Spark Structured Streaming in a Fabric notebook or supported job.
  2. Use the built-in Real-Time hub to discover authorized streams and sources.
  3. Configure an Eventstream with the supported source, filter, and destination.
  4. Create a Real-Time Dashboard backed by suitable Eventhouse queries.

Explanation

Correct Answer

C:

Eventstream provides graphical ingestion and stream operations without requiring a custom Python consumer. Publish a test event and inspect the destination.

Incorrect Answers

A:

Structured Streaming supports programmatic transformations, state, watermarks, and transactional Delta sinks.

B:

Real-Time hub is a discovery entry point, not a separate streaming pipeline that must be manually created.

D:

Real-Time Dashboard visualizes live or recent KQL results, while the event store holds the underlying history.

Question 236

A plant manager wants alerts when equipment telemetry crosses a threshold, not a hand-built polling job. Which component suits the response?

  1. Create a KQL queryset associated with the relevant Eventhouse database.
  2. Use Eventstream ingestion into Eventhouse KQL tables.
  3. Create a Real-Time Dashboard backed by suitable Eventhouse queries.
  4. Feed the stream into Fabric Activator with a rule for the threshold.

Explanation

Correct Answer

D:

Activator evaluates live data conditions and can trigger approved notifications or follow-on actions. Send above- and below-threshold test readings.

Incorrect Answers

A:

KQL querysets support writing, running, and sharing queries against event data.

B:

Eventhouse is designed for event and time-series analysis with KQL, minimizing custom batch query infrastructure.

C:

Real-Time Dashboard visualizes live or recent KQL results, while the event store holds the underlying history.

Question 237

A telemetry team needs real-time visual tiles based on KQL event analysis. What dashboard surface is intended?

  1. Use the built-in Real-Time hub to discover authorized streams and sources.
  2. Create a Real-Time Dashboard backed by suitable Eventhouse queries.
  3. Configure an Eventstream with the supported source, filter, and destination.
  4. Feed the stream into Fabric Activator with a rule for the threshold.

Explanation

Correct Answer

B:

Real-Time Dashboard visualizes live or recent KQL results, while the event store holds the underlying history. Confirm the tiles refresh with incoming test events.

Incorrect Answers

A:

Real-Time hub is a discovery entry point, not a separate streaming pipeline that must be manually created.

C:

Eventstream provides graphical ingestion and stream operations without requiring a custom Python consumer.

D:

Activator evaluates live data conditions and can trigger approved notifications or follow-on actions.

Question 238

An engineer needs a reusable exploratory query workspace over an Eventhouse database. Which item provides it?

  1. Configure an Eventstream with the supported source, filter, and destination.
  2. Use the built-in Real-Time hub to discover authorized streams and sources.
  3. Create a KQL queryset associated with the relevant Eventhouse database.
  4. Use Spark Structured Streaming in a Fabric notebook or supported job.

Explanation

Correct Answer

C:

KQL querysets support writing, running, and sharing queries against event data. Run and save a controlled diagnostic KQL query.

Incorrect Answers

A:

Eventstream provides graphical ingestion and stream operations without requiring a custom Python consumer.

B:

Real-Time hub is a discovery entry point, not a separate streaming pipeline that must be manually created.

D:

Structured Streaming supports programmatic transformations, state, watermarks, and transactional Delta sinks.

Question 239

An analyst wants to discover available live streams without building a new stream ingestion item. Which existing entry point should they use?

  1. Configure an Eventstream with the supported source, filter, and destination.
  2. Use Eventstream ingestion into Eventhouse KQL tables.
  3. Feed the stream into Fabric Activator with a rule for the threshold.
  4. Use the built-in Real-Time hub to discover authorized streams and sources.

Explanation

Correct Answer

D:

Real-Time hub is a discovery entry point, not a separate streaming pipeline that must be manually created. Confirm the user sees only intended stream sources.

Incorrect Answers

A:

Eventstream provides graphical ingestion and stream operations without requiring a custom Python consumer.

B:

Eventhouse is designed for event and time-series analysis with KQL, minimizing custom batch query infrastructure.

C:

Activator evaluates live data conditions and can trigger approved notifications or follow-on actions.

Question 240

Eventhouse must support frequent indexed searches over newly ingested telemetry. Where should the events be stored?

  1. Check shortcut connection and effective permissions to the referenced target.
  2. Create a OneLake shortcut in a KQL database and query it using external_table().
  3. Alter the external Delta table schema to include the new source column or re-enable automatic schema synchronization.
  4. Ingest into native Eventhouse KQL tables rather than relying solely on external shortcut reads.

Explanation

Correct Answer

D:

Native ingestion supplies Eventhouse indexing and storage optimization for repeated low-latency event queries. Measure filter latency against a representative dataset.

Incorrect Answers

A:

Creating an external table reference does not bypass authorization for the source data.

B:

KQL database shortcuts expose external data as queryable tables without making a native ingested copy.

C:

A KQL external table may require updated metadata after Delta schema changes; its logical link alone does not guarantee every consumer schema stays synchronized.

Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Practice Tests