Microsoft DP-700 Practice Test 11
Question 100
Investigators must attribute unexpected access to a shared item. Which linkage is essential?
- Confirm the audit source's actual retention and available time range before assuming six months are present.
- Summarize tenant-wide audit event counts without filtering for the shared item, actor identity or access timestamp.
- Correlate audited item access and sharing events with the identity and event time.
- Inspect the pipeline run and activity details in the Fabric monitoring experience.
Explanation
Correct Answer
C:Use the relevant Fabric or Microsoft Purview audit events and correlate their actor, action, target-item identifier and timestamp. If authorization changed, align sharing/permission history with the access event; disclose gaps if the audit store did not retain the period.
Incorrect Answers
A:Retention depends on the configured auditing platform and licensing; a current log view does not prove complete historic coverage. Access investigations require actor, target, and time; a data row count alone cannot establish who changed sharing. Match the event to the item and relevant principal.
B:Aggregate audit counts cannot prove who accessed a specific shared item. Filter supported audit events by the target item and event time, then correlate actor identity and action with sharing changes before attributing the incident.
D:Operational execution logs reveal failing activities; administrative audit logs focus on user and system actions rather than full run diagnostics. Access investigations require actor, target, and time; a data row count alone cannot establish who changed sharing. Match the event to the item and relevant principal.
Question 101
A governance review finds that item endorsement changed without documented approval. Which data should the owner examine?
- Correlate audited item access and sharing events with the identity and event time.
- Review the available governance/audit event trail and the endorsement-authorized identities.
- Inspect the pipeline run and activity details in the Fabric monitoring experience.
- Confirm the audit source's actual retention and available time range before assuming six months are present.
Explanation
Correct Answer
B:Certification or promotion changes should be attributable to approved stewards, not inferred from the item's current display state. Compare actor membership with the endorsement policy.
Incorrect Answers
A:Access investigations require actor, target, and time; a data row count alone cannot establish who changed sharing.
C:Operational execution logs reveal failing activities; administrative audit logs focus on user and system actions rather than full run diagnostics.
D:Retention depends on the configured auditing platform and licensing; a current log view does not prove complete historic coverage.
Question 102
An administrator exports audit records for a six-month review. What must they validate first?
- Confirm the audit source's actual retention and available time range before assuming six months are present.
- Search the supported activity audit trail for the specific deletion event and actor.
- Correlate audited item access and sharing events with the identity and event time.
- Inspect the pipeline run and activity details in the Fabric monitoring experience.
Explanation
Correct Answer
A:Retention depends on the configured auditing platform and licensing; a current log view does not prove complete historic coverage. Check the oldest available event and retention configuration.
Incorrect Answers
B:An absent item might result from deletion, relocation, or permissions; the audit event can distinguish an attributable operation.
C:Access investigations require actor, target, and time; a data row count alone cannot establish who changed sharing.
D:Operational execution logs reveal failing activities; administrative audit logs focus on user and system actions rather than full run diagnostics.
Question 103
A user denies deleting a Fabric asset. What is a stronger initial investigation method than guessing from missing data?
- Confirm the audit source's actual retention and available time range before assuming six months are present.
- Correlate audited item access and sharing events with the identity and event time.
- Review workspace membership records together with the corresponding audit events.
- Search the supported activity audit trail for the specific deletion event and actor.
Explanation
Correct Answer
D:An absent item might result from deletion, relocation, or permissions; the audit event can distinguish an attributable operation. Compare the event time with workspace change history.
Incorrect Answers
A:Retention depends on the configured auditing platform and licensing; a current log view does not prove complete historic coverage.
B:Access investigations require actor, target, and time; a data row count alone cannot establish who changed sharing.
C:Current membership shows who has rights now; audit evidence supports who granted or removed them over time.
Question 104
A security policy requires evidence of privileged workspace membership changes. What should be correlated?
- Review workspace membership records together with the corresponding audit events.
- Use the supported Fabric/Microsoft Purview audit log records for relevant administrative and sharing events.
- Search the supported activity audit trail for the specific deletion event and actor.
- Inspect the pipeline run and activity details in the Fabric monitoring experience.
Explanation
Correct Answer
A:Current membership shows who has rights now; audit evidence supports who granted or removed them over time. Reconcile a known role change against both views.
Incorrect Answers
B:Audit logs provide attributable activity records; ordinary pipeline run metrics are not a substitute for governance event history.
C:An absent item might result from deletion, relocation, or permissions; the audit event can distinguish an attributable operation.
D:Operational execution logs reveal failing activities; administrative audit logs focus on user and system actions rather than full run diagnostics.
Question 105
A citizen developer needs point-and-click Power Query transformations from an Excel source into a lakehouse. Which Fabric item is the closest fit?
- Create a data pipeline with parameterized activities and retry/condition logic.
- Use a Fabric Spark notebook with PySpark transformations.
- Develop the transformation in a Spark notebook attached to an appropriate environment.
- Build a Dataflow Gen2 with the necessary Power Query steps and configured destination.
Explanation
Correct Answer
D:Dataflow Gen2 is designed for low-code transformations with reusable refresh behavior and data destinations. Preview the transformed rows and validate the refresh.
Incorrect Answers
A:Pipeline orchestration handles multi-item dependencies and execution control without embedding scheduling logic in each notebook.
B:A notebook supports programmatic distributed processing and custom Python logic beyond basic visual data shaping.
C:Notebooks express code-heavy distributed transformations and can share governed libraries through environments.
Question 106
A nightly process must copy files and then execute a notebook only if ingestion succeeds. Which primary orchestrator should coordinate it?
- Build a Dataflow Gen2 with the necessary Power Query steps and configured destination.
- Use Dataflow Gen2 Power Query transformations rather than custom Spark code.
- Use a Fabric data pipeline with dependent activities.
- Use a Fabric Spark notebook with PySpark transformations.
Explanation
Correct Answer
C:Pipelines coordinate data movement and conditional execution across activities, unlike a transformation query by itself. Inspect run dependencies and failure handling.
Incorrect Answers
A:Dataflow Gen2 is designed for low-code transformations with reusable refresh behavior and data destinations.
B:Low-code Power Query is appropriate for modest connector-driven shaping without a bespoke distributed program.
D:A notebook supports programmatic distributed processing and custom Python logic beyond basic visual data shaping.
Question 107
A data engineer must implement a complex Python data-cleaning algorithm over large Delta files. Which engine should run the transformations?
- Use Dataflow Gen2 and choose its supported warehouse destination.
- Use a Fabric data pipeline with dependent activities.
- Build a Dataflow Gen2 with the necessary Power Query steps and configured destination.
- Use a Fabric Spark notebook with PySpark transformations.
Explanation
Correct Answer
D:A notebook supports programmatic distributed processing and custom Python logic beyond basic visual data shaping. Validate output rows on an isolated test input.
Incorrect Answers
A:A dataflow supports connector-based Power Query shaping and destinations without authoring an orchestration graph.
B:Pipelines coordinate data movement and conditional execution across activities, unlike a transformation query by itself.
C:Dataflow Gen2 is designed for low-code transformations with reusable refresh behavior and data destinations.
Question 108
A small analyst-owned source requires straightforward merges and type conversions with no custom code. Which path minimizes maintenance?
- Use a Fabric data pipeline with dependent activities.
- Create a data pipeline with parameterized activities and retry/condition logic.
- Use a Fabric Spark notebook with PySpark transformations.
- Use Dataflow Gen2 Power Query transformations rather than custom Spark code.
Explanation
Correct Answer
D:Low-code Power Query is appropriate for modest connector-driven shaping without a bespoke distributed program. Inspect refresh results and destination types.
Incorrect Answers
A:Pipelines coordinate data movement and conditional execution across activities, unlike a transformation query by itself.
B:Pipeline orchestration handles multi-item dependencies and execution control without embedding scheduling logic in each notebook.
C:A notebook supports programmatic distributed processing and custom Python logic beyond basic visual data shaping.
Question 109
A solution must parameterize and sequence five different Fabric jobs with retries. Which item acts as the controller?
- Use a Fabric Spark notebook with PySpark transformations.
- Create a data pipeline with parameterized activities and retry/condition logic.
- Use Dataflow Gen2 Power Query transformations rather than custom Spark code.
- Build a Dataflow Gen2 with the necessary Power Query steps and configured destination.
Explanation
Correct Answer
B:Pipeline orchestration handles multi-item dependencies and execution control without embedding scheduling logic in each notebook. Inspect a failed-run retry path.
Incorrect Answers
A:A notebook supports programmatic distributed processing and custom Python logic beyond basic visual data shaping.
C:Low-code Power Query is appropriate for modest connector-driven shaping without a bespoke distributed program.
D:Dataflow Gen2 is designed for low-code transformations with reusable refresh behavior and data destinations.
Question 110
A scientist wants to reuse Python libraries and implement multi-stage feature extraction in Spark. Which item should own the code?
- Use Dataflow Gen2 and choose its supported warehouse destination.
- Develop the transformation in a Spark notebook attached to an appropriate environment.
- Create a data pipeline with parameterized activities and retry/condition logic.
- Build a Dataflow Gen2 with the necessary Power Query steps and configured destination.
Explanation
Correct Answer
B:Notebooks express code-heavy distributed transformations and can share governed libraries through environments. Compare resulting features with expected test cases.
Incorrect Answers
A:A dataflow supports connector-based Power Query shaping and destinations without authoring an orchestration graph.
C:Pipeline orchestration handles multi-item dependencies and execution control without embedding scheduling logic in each notebook.
D:Dataflow Gen2 is designed for low-code transformations with reusable refresh behavior and data destinations.
