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The Fabric engineering team must address this requirement: A source SQL database has a reliable last_modified_utc column and nightly changes should not require full extraction. Which implementation meets the requirement and provides this acceptance evidence: an unchanged second run loads no new rows and a late updated row is captured?
A high-watermark incremental load fetches changes since the last committed boundary. Verify that an unchanged second run loads no new rows and a late updated row is captured.
Type 1 updates replace the current attribute without retaining prior versions. However, this alone does not show that an unchanged second run loads no new rows and a late updated row is captured.
Streaming architectures ingest and query events with low delay compared with scheduled batch loads. However, this alone does not show that an unchanged second run loads no new rows and a late updated row is captured.
Unknown dimension handling preserves fact integrity without fabricating dimension matches. However, this alone does not show that an unchanged second run loads no new rows and a late updated row is captured.
The reviewer will approve this deployment only when it fixes both issues: (a) A stateful Spark stream fails after a new grouping key was added but the old checkpoint was reused. (b) An internal shortcut works for workspace admins but not for a new Viewer. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: State schema or operator changes can invalidate persisted checkpoints. Step 2 resolves requirement B: Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization. Both acceptance tests are required: the new version resumes from the intended offset without silently skipping events; and the Viewer reads only intended shortcut data after scoped access is granted.
State schema or operator changes can invalidate persisted checkpoints. Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization. The proposed steps (Review the destination mapping and normalize incompatible event types before ingestion; select the intended replace/upsert/write behavior and add idempotence checks) address other capabilities instead of this required pair.
Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization. The other proposed action (Review the destination mapping and normalize incompatible event types before ingestion) does not implement the first control. State schema or operator changes can invalidate persisted checkpoints.
State schema or operator changes can invalidate persisted checkpoints. The other proposed action (select the intended replace/upsert/write behavior and add idempotence checks) addresses a different operation. Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization.
A Fabric engineer has implemented the following change: Record both source event time and ingestion completion time in freshness telemetry. The original requirement was: A service-level report uses event arrival time while business users measure latency from event creation. Which observation best validates that the change works?
Event-to-query latency differs from service-only ingest processing time.
Cross-stage identifiers allow causal diagnosis across independent Fabric item types. It does not show that the report computes the required end-to-end delay for a delayed sample event.
A healthy final status does not rule out performance regression. It does not show that the report computes the required end-to-end delay for a delayed sample event.
Operational alerting turns run failures into actionable notifications rather than manual polling. It does not show that the report computes the required end-to-end delay for a delayed sample event.
The operations team has applied: Query supported Query Insights execution history for status, timing and submitter context. Its purpose was: A warehouse analyst wants recent failed and canceled SQL statements correlated to users. Which of the following provides the strongest direct evidence of success?
SQL query monitoring provides query-level facts rather than only Fabric item health.
Cross-stage identifiers allow causal diagnosis across independent Fabric item types. It does not show that the selected canceled query is traced to its recorded request and user.
Notebook failures can originate in the Spark job rather than pipeline orchestration. It does not show that the selected canceled query is traced to its recorded request and user.
Editing/publishing validation and scheduled refresh are distinct operational states. It does not show that the selected canceled query is traced to its recorded request and user.
The project has two blockers. Blocker A: Sensitive data residency rules require real-time shortcut query acceleration to stay inside an approved region. Blocker B: An engineer plans to solve a batch nightly sales reconciliation using Eventstream solely because the records are in JSON. What pair of actions resolves both blockers?
Step 1 resolves requirement A: Accelerated shortcut caching has regional residency implications separate from source metadata. Step 2 resolves requirement B: Streaming imposes state and always-on operation costs that a finite batch task may not need. Both acceptance tests are required: the compliance check confirms accelerated data stays in the designated allowed region; and one complete nightly batch reconciles totals without unnecessary continuous event processing.
Streaming imposes state and always-on operation costs that a finite batch task may not need. The other proposed action (Choose a supported stream enrichment/join pattern and define time/state limits) does not implement the first control. Accelerated shortcut caching has regional residency implications separate from source metadata.
Accelerated shortcut caching has regional residency implications separate from source metadata. The other proposed action (check Eventstream destination health, bindings, event schema and routing rather than changing source timestamps first) addresses a different operation. Streaming imposes state and always-on operation costs that a finite batch task may not need.
Accelerated shortcut caching has regional residency implications separate from source metadata. Streaming imposes state and always-on operation costs that a finite batch task may not need. The proposed steps (Choose a supported stream enrichment/join pattern and define time/state limits; check Eventstream destination health, bindings, event schema and routing rather than changing source timestamps first) address other capabilities instead of this required pair.
A Fabric data engineering release must address both of these independent requirements: (1) A windowed Spark stream discards valid events delayed by 12 minutes because the watermark is set to only two minutes. (2) A KQL query must identify missing hourly intervals in a telemetry series rather than just list hours containing events. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: A too-short watermark can exclude valid late events; a longer delay retains more state. Step 2 resolves requirement B: Make-series generates regular time bins, including configured defaults for empty periods. Both acceptance tests are required: representative twelve-minute-late events are included under the approved configuration; and the output contains the missing hour as a zero or null according to design.
A too-short watermark can exclude valid late events; a longer delay retains more state. Make-series generates regular time bins, including configured defaults for empty periods. The proposed steps (Create a Fabric Eventstream with the supported source and Eventhouse destination; use supported Eventstream transformation operations before the selected destination) address other capabilities instead of this required pair.
A too-short watermark can exclude valid late events; a longer delay retains more state. The other proposed action (use supported Eventstream transformation operations before the selected destination) addresses a different operation. Make-series generates regular time bins, including configured defaults for empty periods.
Make-series generates regular time bins, including configured defaults for empty periods. The other proposed action (Create a Fabric Eventstream with the supported source and Eventhouse destination) does not implement the first control. A too-short watermark can exclude valid late events; a longer delay retains more state.
The data platform team needs a narrowly scoped fix: Engineers need complex PySpark transformations and Delta MERGE logic on a large Lakehouse table. Which approach satisfies the documented test that a parameterized run updates the expected Delta table without full replacement?
Spark code supports programmable distributed transforms and Delta upsert patterns. Verify that a parameterized run updates the expected Delta table without full replacement.
Conditional orchestration can route execution based on prior activity outputs. However, this alone does not show that a parameterized run updates the expected Delta table without full replacement.
Loop activities apply repeatable steps to each collection element. However, this alone does not show that a parameterized run updates the expected Delta table without full replacement.
Controlled parallelism protects external API quotas without reducing correctness of the load. However, this alone does not show that a parameterized run updates the expected Delta table without full replacement.
The operations team has applied: Define the fact grain and map natural source keys to conformed dimension surrogate keys. Its purpose was: A retailer needs a fact table with order-line grain and product/customer dimension keys for analysis. Which of the following provides the strongest direct evidence of success?
Dimensional modeling separates measurable events from descriptive attributes at a declared grain.
Bronze history and curated current-state views serve different retention and reporting needs. It does not show that two items on one order produce two fact rows with valid dimensional references.
Checkpoint commit order is essential for recoverability and at-least-once source reads. It does not show that two items on one order produce two fact rows with valid dimensional references.
Late-arriving updates require revisiting a lookback period rather than processing only new wall-clock dates. It does not show that two items on one order produce two fact rows with valid dimensional references.
A Fabric data engineer receives a change request: A team uses deployment pipelines but leaves the production environment connected to the feature Git branch. Which action best produces this measurable result: production reflects approved promoted definitions rather than unreviewed feature commits?
Git sync and environment deployment are different workflows with different source-of-truth expectations. Verify that production reflects approved promoted definitions rather than unreviewed feature commits.
A linked branch does not automatically mean every local workspace change has been committed. However, this alone does not show that production reflects approved promoted definitions rather than unreviewed feature commits.
Notebook definitions can depend on environment-level libraries and Spark versions. However, this alone does not show that production reflects approved promoted definitions rather than unreviewed feature commits.
Fabric Git integration versions item definitions so changes can be reviewed and traced. However, this alone does not show that production reflects approved promoted definitions rather than unreviewed feature commits.
The reviewer will approve this deployment only when it fixes both issues: (a) A Dataflow Gen2 refresh fails with a data destination validation error after a source column was renamed. (b) An Eventhouse suddenly contains no new records because its ingestion source connection expired. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: Dataflow source/query schema and destination mapping must agree. Step 2 resolves requirement B: Expired source access interrupts ingestion even if the KQL query syntax is valid. Both acceptance tests are required: the refresh succeeds and fields land in the correct typed columns; and a new source event appears after the connection is restored.
Dataflow source/query schema and destination mapping must agree. The other proposed action (inspect the transformation/filter conditions and compare source and derived-stream event counters) addresses a different operation. Expired source access interrupts ingestion even if the KQL query syntax is valid.
Dataflow source/query schema and destination mapping must agree. Expired source access interrupts ingestion even if the KQL query syntax is valid. The proposed steps (Repair the approved shortcut connection authorization and validate source data permissions; inspect the transformation/filter conditions and compare source and derived-stream event counters) address other capabilities instead of this required pair.
Expired source access interrupts ingestion even if the KQL query syntax is valid. The other proposed action (Repair the approved shortcut connection authorization and validate source data permissions) does not implement the first control. Dataflow source/query schema and destination mapping must agree.
A Fabric release must resolve two independent technical requirements. Requirement A: A source table has no change timestamp and no CDC capability, but changes must be detected reliably each night. Requirement B: A streaming source delivers messages at least once after network retries. Select TWO actions that directly satisfy both requirements.
Transport retry can redeliver messages even when the consumer recovered correctly. Confirm that a redelivered event does not increase distinct business-event count.
Incremental detection must be based on an actual observable change signal. Confirm that modified and removed rows are recognized when two snapshots differ.
Namespace and batch isolation prevent filename collisions across producers.
A file-ready boundary prevents reading partially delivered source data.
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