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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 28

Question 270

A Spark streaming job writes to Delta and must keep output progress safe if a microbatch is retried. What should the engineer implement first to satisfy the stated need?

  1. Materialize required transformations into native KQL tables before applying update policies.
  2. Use a stateful streaming join with a valid event-time bound and watermark policy.
  3. Use a checkpoint and an idempotent sink write strategy with stable business identifiers.
  4. Use deduplication keyed on event ID within a defined streaming state policy.

Explanation

Correct Answer

C:

A checkpoint plus idempotent behavior reduces repeated business effects after failure.

Incorrect Answers

A:

Accelerated external shortcuts do not support native-table update policies or materialized views.

B:

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

D:

Keyed streaming deduplication limits duplicate business contributions.

Question 271

A developer and repository both modify the same pipeline definition after the last synchronization. Which engineering decision is most suitable for this specific situation?

  1. Resolve the Git conflict by reviewing both changes before synchronizing.
  2. Retain the external shortcut target when promoting the lakehouse definition.
  3. Commit the environment configuration and notebook changes together when both are supported.
  4. Use the deployment pipeline stage comparison to review pending item changes.

Explanation

Correct Answer

A:

Divergent definitions must be reconciled; blindly overwriting loses one side's edits.

Incorrect Answers

B:

External OneLake shortcut definitions keep their referenced external targets during supported deployment.

C:

Co-versioning dependencies avoids deploying code without required runtime configuration.

D:

Stage comparison identifies supported items that changed or are missing between stages.

Question 272

A supported Dataflow Gen2 performs bulk movement with very few transforms yet refresh is slow. Which engineering decision is most suitable for this specific situation?

  1. Run Delta OPTIMIZE compaction.
  2. Coalesce appropriately before writing.
  3. Use eligible Dataflow Gen2 Fast Copy.
  4. Reorder supported steps to preserve query folding.

Explanation

Correct Answer

C:

Fast Copy can parallelize supported movement. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Compaction combines small files into more efficient files.

B:

Reducing output partitions prevents excessive small files.

D:

Folding pushes selective work into the source.

Question 273

Call center agents may query an email field but should normally see only obfuscated characters rather than the full address. Which action best addresses the requirement?

  1. Use column-level access controls to deny the Salary column to analysts.
  2. Implement T-SQL row-level security with a filtering predicate and security policy.
  3. Configure a partial dynamic data mask on the email column for nonprivileged users.
  4. Apply column-level constraints in a read-only OneLake security role.

Explanation

Correct Answer

C:

Dynamic data masking changes the query result representation for users without unmask privileges.

Incorrect Answers

A:

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

B:

Row-level security restricts which records are returned based on the query context.

D:

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

Question 274

In a Microsoft Fabric solution, a release must preserve production warehouse data while updating reusable SQL views to a new version. Which configuration or implementation should the engineer choose?

  1. Deploy lakehouse metadata while treating table data as separately managed.
  2. Deploy the reviewed SQL schema change rather than replacing warehouse data.
  3. Connect the development workspace to a supported Git repository and work through reviewed branches.
  4. Inspect and correct dependency mapping independently of the newly created lakehouse.

Explanation

Correct Answer

B:

Schema definitions and physical data are distinct release concerns.

Incorrect Answers

A:

Lakehouse deployment and Git operations track metadata and definitions, not lakehouse table contents.

C:

Git integration tracks supported Fabric item definitions for collaborative version control.

D:

Default deployment behavior may provision an empty lakehouse even when references are mapped elsewhere.

Question 275

In a Microsoft Fabric solution, a report containing personal data must carry the organization's information classification across supported Fabric items. Which configuration or implementation should the engineer choose?

  1. Apply the approved sensitivity label to the item.
  2. Apply SQL object-level permissions to that procedure.
  3. Maintain the specific item share rather than granting workspace Contributor.
  4. Apply the appropriate Fabric item endorsement such as Certified after governance review.

Explanation

Correct Answer

A:

Sensitivity labels classify and govern sensitive information independently of workspace membership.

Incorrect Answers

B:

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

C:

Item access is narrower than workspace roles and avoids unnecessary creation and modification rights.

D:

Endorsement identifies trusted assets for discovery and is distinct from row-level permissions.

Question 276

In a Microsoft Fabric solution, high-concurrency notebooks are run by the same user and use matching compute, but each attaches to a different default lakehouse. Which configuration or implementation should the engineer choose?

  1. Set the same default lakehouse on notebooks intended to share the session.
  2. Publish a Fabric environment with the common library and attach it to the notebooks.
  3. Select the intended pool configuration in the notebook's environment Compute settings.
  4. Set the chosen published environment as the workspace default environment.

Explanation

Correct Answer

A:

A matching default lakehouse is a prerequisite for sharing a Spark session.

Incorrect Answers

B:

Environment items centrally define reusable libraries and Spark runtime settings.

C:

Environment compute configuration can specify pool resources when workspace overrides are allowed.

D:

Workspace default can inherit a selected environment's compute and library configuration.

Question 277

A telemetry service must distribute source data to different sinks with independently selected filtering rules. Which action best addresses the requirement?

  1. Route parse failures to an error or quarantine path with identifying metadata.
  2. Create distinct filtered branches or transformations in Eventstream.
  3. Extract and cast the dynamic field to the required scalar type in KQL.
  4. Create a suitable KQL materialized view for the frequently reused aggregation.

Explanation

Correct Answer

B:

Per-branch transformations can route tailored subsets of an incoming stream.

Incorrect Answers

A:

Separating invalid events preserves traceability and protects curated results.

C:

KQL dynamic fields need explicit field access and conversion for numeric operations.

D:

Materialized views maintain precomputed results for supported KQL aggregation patterns.

Question 278

A source appends new transactions and exposes a trustworthy monotonically increasing transaction ID. What should the engineer implement first to satisfy the stated need?

  1. Load rows above the persisted high-water transaction ID and advance the watermark after success.
  2. Record source currency, rate context, and transaction-time reference in the loading design.
  3. Deduplicate using the complete business key of transaction ID and line number.
  4. Assign a controlled unknown or inferred dimension member until the dimension becomes available.

Explanation

Correct Answer

A:

A durable high-water mark limits later loads to unseen append-only records.

Incorrect Answers

B:

Preserving conversion context avoids misinterpreting historic amounts under current rates.

C:

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

D:

An unknown or inferred member preserves fact referential integrity for late dimensions.

Question 279

In a Microsoft Fabric solution, a Spark DataFrame job raises an unresolved-column AnalysisException after an upstream schema rename. Which configuration or implementation should the engineer choose?

  1. Update the KQL column reference or project a compatible alias.
  2. Update the event schema parsing and transformation expressions for the new structure.
  3. Update the column reference or apply an explicit rename mapping to the new schema.
  4. Disable the acceleration policy, update the external table schema, and re-enable when required.

Explanation

Correct Answer

C:

A missing field name cannot be resolved by the Spark analyzer.

Incorrect Answers

A:

KQL queries must reference fields that exist in the current table schema.

B:

Transformations need field paths consistent with actual source messages.

D:

Breaking Delta schema changes require policy and external schema reconciliation.

Question 280

Modelers mainly use T-SQL views and relational joins over structured enterprise facts. Which action best addresses the requirement?

  1. Choose a Fabric Warehouse for the SQL-first model.
  2. Choose a Fabric lakehouse for file-oriented analytical storage.
  3. Join facts to reference rows by stable product key.
  4. Run the logic in a Fabric PySpark notebook.

Explanation

Correct Answer

A:

Warehouse supports relational tables and T-SQL analytical workloads.

Incorrect Answers

B:

Lakehouses combine OneLake files, Spark Delta tables, and a SQL endpoint.

C:

Enrichment joins add trusted attributes from matching keys.

D:

Notebooks support custom distributed transformations and environments.