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

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Microsoft DP-700 Practice Test 11

Question 100

Power Query transformations in Dataflow Gen2 prevent an eligible SQL source filter from folding. What should the engineer implement first to satisfy the stated need?

  1. Enable supported query acceleration.
  2. Tune supported ingestion batching policy for the feed.
  3. Reorder supported steps to preserve query folding.
  4. Adopt supported declarative liquid clustering.

Explanation

Correct Answer

C:

Folding pushes selective work into the source. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Acceleration caches eligible external data for faster KQL queries.

B:

Suitable batching balances ingest overhead with freshness.

D:

Liquid clustering persists chosen columns in table metadata.

Question 101

A Spark pipeline groups an entire table before applying a highly selective filter. Which action best addresses the requirement?

  1. Adopt supported declarative liquid clustering.
  2. Filter unwanted types before the destination.
  3. Push selective filters before the expensive join.
  4. Push the filter before the groupBy shuffle.

Explanation

Correct Answer

D:

Earlier row reduction lowers shuffle volume. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Liquid clustering persists chosen columns in table metadata.

B:

Earlier stream filtering reduces unnecessary downstream traffic.

C:

Earlier predicates reduce processed rows.

Question 102

An enormous Delta fact table is queried primarily with order-date predicates, and the table is still stored in an unoptimized file layout with scans across unnecessary dates. Which single implementation most directly meets the stated requirement?

  1. Parse files and write them to registered Delta tables.
  2. Create an internal OneLake shortcut to the table.
  3. Use Delta MERGE keyed by order ID.
  4. Apply a suitable order-date storage layout, such as reasonable date partitioning where appropriate, and ensure queries preserve pruning.

Explanation

Correct Answer

D:

A suitable data layout and eligible date filters allow Spark to skip irrelevant file ranges or partitions rather than scan the entire table. The direct validation would be: The query execution plan shows only the relevant order-date partitions or file ranges scanned while returning unchanged results.

Incorrect Answers

A:

Files storage is separate from registered lakehouse tables.

B:

Shortcuts reference existing OneLake data without copying files.

C:

MERGE handles matched updates and unmatched inserts.

Question 103

A restricted dataset contains columns requiring different access privileges, and a schema-enabled lakehouse uses OneLake security. Which engineering decision is most suitable for this specific situation?

  1. Use column-level access controls to deny the Salary column to analysts.
  2. Apply SQL object-level permissions to that procedure.
  3. Apply column-level constraints in a read-only OneLake security role.
  4. Review supported audit events for role and permission changes.

Explanation

Correct Answer

C:

OneLake Read roles can constrain selected columns for the specified members.

Incorrect Answers

A:

Column-level security restricts permission to access specific columns rather than merely displaying masked values.

B:

Object-level security controls access to entire objects such as views or procedures.

D:

Audit logs provide available governance activity history for investigation.

Question 104

A data team wants to refresh three independent data sources visually through Power Query transformations and write cleansed outputs without maintaining Python code. What should the engineer implement first to satisfy the stated need?

  1. Place both notebook activities on parallel branches after the staging success dependency.
  2. Expose the business date as a pipeline parameter and supply it at invocation.
  3. Place the dataflow activity upstream of the notebook in the pipeline dependency graph.
  4. Use Dataflow Gen2 with Power Query transformations and configured destinations.

Explanation

Correct Answer

D:

Dataflow Gen2 is the low-code visual data-shaping option for supported source connectors.

Incorrect Answers

A:

Parallel branches enable independent work once common prerequisites are met.

B:

A runtime parameter separates job logic from each run's effective processing date.

C:

Explicit dependencies prevent processing before upstream dataflow completion.

Question 105

In a Microsoft Fabric solution, two operational systems use different identifiers for the same business customer but analytics needs one dimension identity. Which configuration or implementation should the engineer choose?

  1. Map source business identities to a managed surrogate key in the conformed customer dimension.
  2. Deduplicate using the complete business key of transaction ID and line number.
  3. Route rows with missing mandatory business keys to a quarantine table.
  4. Implement slowly changing dimension Type 2 versioned records with effective dates.

Explanation

Correct Answer

A:

A surrogate key unifies business entities without depending on mutable external IDs.

Incorrect Answers

B:

A composite key matches the actual record identity rather than a locally repeated attribute.

C:

Quarantine preserves invalid input for review without corrupting deduplication.

D:

Type 2 preserves previous attribute values by storing dated versions.

Question 106

An orchestration activity produces a small list of table names and a downstream Copy activity must select one name inside a loop. Which action best addresses the requirement?

  1. Reference the current loop item through the pipeline expression builder.
  2. Invoke the reusable pipeline through an Execute Pipeline activity.
  3. Define pipeline parameters and reference them with dynamic content expressions.
  4. Inspect the pipeline's individual activity runs and their error outputs.

Explanation

Correct Answer

A:

ForEach item expressions bind an activity's dynamic value to the current iteration.

Incorrect Answers

B:

Child pipelines allow shared orchestration logic without cloning all activities.

C:

Parameters make the same pipeline definition reusable with different runtime values.

D:

Activity-level results expose the step and message causing an orchestration failure.

Question 107

In a Microsoft Fabric solution, a real-time application must retain incoming events as native KQL tables for repeated low-latency investigations. Which configuration or implementation should the engineer choose?

  1. Use a stateful streaming join with a valid event-time bound and watermark policy.
  2. Define a KQL update policy to transform ingestion into a target native table.
  3. Ingest events into an Eventhouse native table instead of querying only an external shortcut.
  4. Create an Eventstream route into an Eventhouse KQL table.

Explanation

Correct Answer

C:

Native Eventhouse ingestion supports indexed real-time analytics over event records.

Incorrect Answers

A:

Bounded joins limit held state and match related events by key and time.

B:

Update policies can transform or route data at ingestion to suitable native destinations.

D:

Eventstream can ingest and route streaming data to a KQL destination.

Question 108

In a Microsoft Fabric solution, one telemetry feed must supply both a near-real-time KQL store and a compatible streaming output for further processing. Which configuration or implementation should the engineer choose?

  1. Configure multiple appropriate destinations on the Eventstream.
  2. Choose Eventhouse native ingestion for the hot telemetry layer.
  3. Route parse failures to an error or quarantine path with identifying metadata.
  4. Configure a durable checkpoint location for the streaming query.

Explanation

Correct Answer

A:

An Eventstream can route data from a source to more than one destination.

Incorrect Answers

B:

Native real-time tables avoid the remote-file limitations of an external-only shortcut.

C:

Separating invalid events preserves traceability and protects curated results.

D:

Checkpoint metadata records stream processing progress for restart.

Question 109

An operator wants alerts only when the scheduled nightly pipeline fails. What should the engineer implement first to satisfy the stated need?

  1. Set the schedule's failure-notification recipients.
  2. Send sanitized diagnostic metadata rather than sensitive row contents.
  3. Aggregate pipeline run outcomes by date and failure type.
  4. Compare run parameters, linked connection contexts, and activity outputs across environments.

Explanation

Correct Answer

A:

Scheduled-run notifications report failures for that configured schedule.

Incorrect Answers

B:

Alerts should contain sufficient identifiers without disclosing protected data.

C:

Trend monitoring reveals recurrent errors not visible from a single run.

D:

Contextual run evidence isolates configuration drift.

Question 110

Dense device telemetry must support near-real-time filtering and KQL investigation. Which action best addresses the requirement?

  1. Land the events in an Eventhouse KQL database.
  2. Use summarize max(timestamp) by device_id.
  3. Configure a supported mirrored database.
  4. Select a OneLake shortcut instead of database mirroring.

Explanation

Correct Answer

A:

Eventhouse supports high-throughput time-series ingestion and KQL.

Incorrect Answers

B:

Group-wise max returns each device's latest time.

C:

Mirroring propagates ongoing source changes into analytical tables.

D:

Shortcuts virtualize object access while mirroring replicates changes.