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An auditor reviews the applied Fabric control: Redeploy the last approved item definitions from a known-good version through the governed release process. It was selected to address: A production rollback is needed after a deployment pipeline introduced a regression. Which evidence demonstrates the intended result?
Reverting item definitions is a separate action from restoring underlying data that may have changed.
Least-privilege CI/CD identities reduce lateral impact if automation credentials are compromised. It does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
Source-controlled definitions should not embed reusable production secrets. It does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
Notebook definitions can depend on environment-level libraries and Spark versions. It does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
The data platform team needs a narrowly scoped fix: A warehouse dashboard refreshes slowly and users blame source ingestion without checking actual SQL workload. Which approach satisfies the documented test that the slow query shape and its historical duration are identified?
Query history exposes SQL execution trends distinct from copy/load duration. Verify that the slow query shape and its historical duration are identified.
Queue wait time and execution time require different capacity and performance actions. However, this alone does not show that the slow query shape and its historical duration are identified.
Run provenance identifies the environment that produced a dataset. However, this alone does not show that the slow query shape and its historical duration are identified.
Operational alerting turns run failures into actionable notifications rather than manual polling. However, this alone does not show that the slow query shape and its historical duration are identified.
After a production change, the runbook records: Use a supported make-series query with an appropriate default value and time range. The previous problem was: A KQL query must identify missing hourly intervals in a telemetry series rather than just list hours containing events. What test result confirms effective remediation?
Make-series generates regular time bins, including configured defaults for empty periods.
A too-short watermark can exclude valid late events; a longer delay retains more state. It does not show that the output contains the missing hour as a zero or null according to design.
Eventstream routes real-time events to supported sinks for KQL analytics. It does not show that the output contains the missing hour as a zero or null according to design.
Schema mapping makes raw event properties accessible as KQL columns. It does not show that the output contains the missing hour as a zero or null according to design.
An auditor reviews the applied Fabric control: Use the supported Fabric Monitoring hub with appropriate workspace access. It was selected to address: Several workspaces have pipeline failures, and the operations team needs a cross-workspace run view. Which evidence demonstrates the intended result?
The Monitoring hub aggregates applicable run information across workspace items.
A healthy final status does not rule out performance regression. It does not show that the operator finds the selected failed pipeline without opening each workspace separately.
Run provenance identifies the environment that produced a dataset. It does not show that the operator finds the selected failed pipeline without opening each workspace separately.
Upstream data arrival is separate from semantic model refresh completion. It does not show that the operator finds the selected failed pipeline without opening each workspace separately.
A production analytics workflow has this requirement: A 200 GB nightly file arrives in pieces and the Copy activity starts before all parts are uploaded. What is the most appropriate change if success must be shown by this test: the pipeline starts only after all manifest entries are present and valid?
A file-ready boundary prevents reading partially delivered source data. Verify that the pipeline starts only after all manifest entries are present and valid.
CDC retains insert/update/delete semantics that append-only loading may lose. However, this alone does not show that the pipeline starts only after all manifest entries are present and valid.
Streaming architectures ingest and query events with low delay compared with scheduled batch loads. However, this alone does not show that the pipeline starts only after all manifest entries are present and valid.
Business-key upserts prevent replayed snapshots from multiplying logical records. However, this alone does not show that the pipeline starts only after all manifest entries are present and valid.
A team is working on two related Fabric work items. Work item A: A data warehouse team wants SQL schema objects reviewed as code rather than maintained only through manual portal changes. Work item B: A CI/CD system must promote item definitions to a destination Fabric workspace with no interactive user access. Select TWO specific implementation steps.
Database projects make selected relational schemas deployable and reviewable as source artifacts. Confirm that a reviewed schema change generates the intended database objects in the target environment.
Automated release operations require explicit identity and API permissions independent of interactive workspace ownership. Confirm that a pipeline release updates only the intended workspace and produces an auditable run record.
Fabric Git integration support is item-specific; workspace membership alone is insufficient.
A Git merge does not necessarily update every connected workspace or deployment stage.
The reviewer will approve this deployment only when it fixes both issues: (a) A deployment pipeline promotes a notebook that references a development lakehouse, and production jobs still target development data. (b) Two engineers overwrite each other's notebook edits because they share a single feature branch. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: Promotion of definitions does not imply every environment-specific connection is automatically safe. Step 2 resolves requirement B: Branch-based review resolves competing definition edits before a shared release branch is updated. Both acceptance tests are required: a production test run reads only the approved production lakehouse; and both feature changes are merged with a reviewed history rather than lost updates.
Promotion of definitions does not imply every environment-specific connection is automatically safe. Branch-based review resolves competing definition edits before a shared release branch is updated. The proposed steps (Review deployment-generated schema changes and apply safe migration controls before promotion; version and promote the supported environment and notebook together with valid references) address other capabilities instead of this required pair.
Branch-based review resolves competing definition edits before a shared release branch is updated. The other proposed action (Review deployment-generated schema changes and apply safe migration controls before promotion) does not implement the first control. Promotion of definitions does not imply every environment-specific connection is automatically safe.
Promotion of definitions does not imply every environment-specific connection is automatically safe. The other proposed action (version and promote the supported environment and notebook together with valid references) addresses a different operation. Branch-based review resolves competing definition edits before a shared release branch is updated.
A production analytics workflow has this requirement: An Eventhouse shortcut includes six tables but only one table is needed for KQL and the engineer assumes one shortcut can target all of them. What is the most appropriate change if success must be shown by this test: the intended table is queryable and unrelated source tables are not inadvertently exposed?
KQL OneLake shortcuts target one table or subfolder per shortcut, so broad collection mapping needs separate entries. Verify that the intended table is queryable and unrelated source tables are not inadvertently exposed.
Checkpoints persist streaming offsets, progress and state for recoverable jobs. However, this alone does not show that the intended table is queryable and unrelated source tables are not inadvertently exposed.
KQL binning groups events into fixed time buckets. However, this alone does not show that the intended table is queryable and unrelated source tables are not inadvertently exposed.
Schema mapping makes raw event properties accessible as KQL columns. However, this alone does not show that the intended table is queryable and unrelated source tables are not inadvertently exposed.
The project has two blockers. Blocker A: A pipeline copy lands decimal amounts as strings and analytics totals are incorrect. Blocker B: A Dataflow Gen2 refresh fails after source columns were renamed but the destination still uses old field mappings. What pair of actions resolves both blockers?
Step 1 resolves requirement A: Explicit conversion protects numerical fidelity during ingestion. Step 2 resolves requirement B: Dataflow query shape and destination mapping must remain compatible. Both acceptance tests are required: decimal sums match source totals and malformed values enter an exception path; and the updated refresh loads each field into its intended destination column.
Dataflow query shape and destination mapping must remain compatible. The other proposed action (Evaluate supported Fabric database mirroring for the source and target use case) does not implement the first control. Explicit conversion protects numerical fidelity during ingestion.
Explicit conversion protects numerical fidelity during ingestion. Dataflow query shape and destination mapping must remain compatible. The proposed steps (Evaluate supported Fabric database mirroring for the source and target use case; change authoritative data at the source and query the mirrored replica for analytics) address other capabilities instead of this required pair.
Explicit conversion protects numerical fidelity during ingestion. The other proposed action (change authoritative data at the source and query the mirrored replica for analytics) addresses a different operation. Dataflow query shape and destination mapping must remain compatible.
After a production change, the runbook records: Use a Dataflow Gen2 with Power Query transformations and a configured data destination. The previous problem was: Business analysts need to clean a small table visually with Power Query and load the result into a supported destination. What test result confirms effective remediation?
Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code.
Fan-out/fan-in orchestration enforces completion before downstream publishing. It does not show that the refreshed destination contains the correctly typed and filtered rows.
Scheduled triggers execute a workflow at defined times independent of source change events. It does not show that the refreshed destination contains the correctly typed and filtered rows.
Activity dependencies establish a deterministic execution order rather than relying on visual canvas placement. It does not show that the refreshed destination contains the correctly typed and filtered rows.
An auditor reviews the applied Fabric control: Fix the supported ingestion mapping or source timestamp format and test typed conversion. It was selected to address: An Eventhouse KQL ingestion mapping rejects payloads whose timestamp arrives as an unparseable text field. Which evidence demonstrates the intended result?
KQL ingestion requires compatible column types and valid mapping expressions.
A missing or renamed column breaks Spark analysis before valid execution. It does not show that a test event lands with a correct datetime column and no mapping error.
Unbalanced shuffle partitions can exhaust one executor while others remain underutilized. It does not show that a test event lands with a correct datetime column and no mapping error.
Shortcut path references are not automatically rewritten when source folders are relocated. It does not show that a test event lands with a correct datetime column and no mapping error.
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