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During a Microsoft Fabric deployment review, the following problem is identified: A production rollback is needed after a deployment pipeline introduced a regression. The reviewer requires the following result: the prior approved definitions execute and data recovery, if required, is handled separately. What should the engineer implement?
Reverting item definitions is a separate action from restoring underlying data that may have changed. Verify 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. However, this alone does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
Fabric Git integration support is item-specific; workspace membership alone is insufficient. However, this alone does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
Fabric Git integration versions item definitions so changes can be reviewed and traced. However, this alone does not show that the prior approved definitions execute and data recovery, if required, is handled separately.
An auditor reviews the applied Fabric control: Compare multiple refresh-history durations and per-query execution behavior. It was selected to address: A dataflow has intermittently slow refreshes but its latest status is green. Which evidence demonstrates the intended result?
A healthy final status does not rule out performance regression.
Upstream data arrival is separate from semantic model refresh completion. It does not show that the historical refresh report isolates the query whose duration increased.
Freshness degradation can occur before a hard ingestion failure. It does not show that the historical refresh report isolates the query whose duration increased.
Run-level status does not replace individual activity diagnostics for failure localization. It does not show that the historical refresh report isolates the query whose duration increased.
An auditor reviews the applied Fabric control: Apply appropriate Microsoft Purview sensitivity labels to the supported Fabric items. It was selected to address: A team needs classification metadata for sensitive Fabric warehouse and Lakehouse artifacts visible to governance tools. Which evidence demonstrates the intended result?
Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access.
Union-like access from multiple roles can explain visibility that a single restricted role does not predict. It does not show that the labels appear on the intended items while existing access permissions remain intact.
Item visibility and data-plane read rights can be independently evaluated. It does not show that the labels appear on the intended items while existing access permissions remain intact.
Combining row and column filters in unsupported separate roles can cause query errors. It does not show that the labels appear on the intended items while existing access permissions remain intact.
An enterprise Fabric solution must resolve this issue: A traffic team needs to join near-real-time events to contextual reference data with bounded state. The acceptance criterion is: a controlled key joins to the correct enrichment and idle state remains bounded. Which implementation should be selected?
Streaming joins must control state growth and match event keys appropriately. Verify that a controlled key joins to the correct enrichment and idle state remains bounded.
Materialized aggregations can precompute repeated summaries to reduce query cost. However, this alone does not show that a controlled key joins to the correct enrichment and idle state remains bounded.
A OneLake shortcut exposes external Delta data for KQL querying without a full ingestion copy. However, this alone does not show that a controlled key joins to the correct enrichment and idle state remains bounded.
Overly long watermark delays retain unnecessary window or join state. However, this alone does not show that a controlled key joins to the correct enrichment and idle state remains bounded.
Two data engineering defects have separate acceptance criteria. First: An application writes updates into a mirrored source while engineers propose editing the replicated Fabric target table directly. Second: A structured table has an abnormal count of null product IDs after a CSV import. Which TWO changes are necessary to resolve both?
Ingestion validation separates bad source rows from trustworthy analytic data. Confirm that rows with null keys are excluded or repaired according to policy and counted.
Mirroring is a replication pattern, not a general bidirectional transaction editor. Confirm that the source update propagates and no downstream process attempts unsupported target writes.
Shortcuts present source data under OneLake paths without physically duplicating the source files.
Explicit conversion protects numerical fidelity during ingestion.
The Fabric engineering team must address this requirement: An Eventhouse suddenly contains no new records because its ingestion source connection expired. Which implementation meets the requirement and provides this acceptance evidence: a new source event appears after the connection is restored?
Expired source access interrupts ingestion even if the KQL query syntax is valid. Verify that a new source event appears after the connection is restored.
Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization. However, this alone does not show that a new source event appears after the connection is restored.
Incorrect filter boundaries cause silent logical data loss even with technically successful Copy activity. However, this alone does not show that a new source event appears after the connection is restored.
Scheduled Spark jobs use their declared environment rather than an old notebook session's temporary libraries. However, this alone does not show that a new source event appears after the connection is restored.
A Fabric release must resolve two independent technical requirements. Requirement A: A team connected a Fabric workspace to a Git branch but assumes new uncommitted edits are already safely stored in the repository. Requirement B: A deployment must preserve secret values while moving item definitions into a test environment. Select TWO actions that directly satisfy both requirements.
A linked branch does not automatically mean every local workspace change has been committed. Confirm that the intended item definition appears in the selected commit with no pending uncommitted changes.
Source-controlled definitions should not embed reusable production secrets. Confirm that the test item authenticates with approved test credentials and repository history contains no secrets.
Database project source control can make targeted DDL changes auditable.
Database projects make selected relational schemas deployable and reviewable as source artifacts.
An enterprise Fabric solution must resolve this issue: Auditors need to determine who changed an artifact or accessed supported Fabric resources during a specified interval. The acceptance criterion is: the audit query returns the matching activity with actor and timestamp. Which implementation should be selected?
Supported Fabric audit events expose administrative or item activities without being the same as pipeline data logs. Verify that the audit query returns the matching activity with actor and timestamp.
A broad default data grant can remain effective alongside narrower roles. However, this alone does not show that the audit query returns the matching activity with actor and timestamp.
Member grants broad workspace capabilities that exceed a read-only partner requirement. However, this alone does not show that the audit query returns the matching activity with actor and timestamp.
Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access. However, this alone does not show that the audit query returns the matching activity with actor and timestamp.
Two data engineering defects have separate acceptance criteria. First: A dataflow has intermittently slow refreshes but its latest status is green. Second: A warehouse analyst wants recent failed and canceled SQL statements correlated to users. Which TWO changes are necessary to resolve both?
A healthy final status does not rule out performance regression. Confirm that the historical refresh report isolates the query whose duration increased.
SQL query monitoring provides query-level facts rather than only Fabric item health. Confirm that the selected canceled query is traced to its recorded request and user.
Upstream data arrival is separate from semantic model refresh completion.
Source activity does not prove a downstream destination is consuming messages.
After a production change, the runbook records: Persist both business event timestamp and processing/arrival timestamp in the staging model. The previous problem was: A data team needs to distinguish source transaction time from ingestion processing time for late records. What test result confirms effective remediation?
Event time supports domain analysis and processing time supports operational freshness diagnosis.
Transport retry can redeliver messages even when the consumer recovered correctly. It does not show that a deliberately late event retains its original business timestamp and late arrival metadata.
Streaming architectures ingest and query events with low delay compared with scheduled batch loads. It does not show that a deliberately late event retains its original business timestamp and late arrival metadata.
Bronze ingestion preserves source evidence separately from silver/gold business interpretations. It does not show that a deliberately late event retains its original business timestamp and late arrival metadata.
The operations team has applied: Make foreachBatch writes idempotent using stable event keys, batch IDs or transactional merging. Its purpose was: A foreachBatch streaming sink replays a microbatch after interruption and double-charges business events. Which of the following provides the strongest direct evidence of success?
Custom microbatch side effects can rerun; checkpoint alone does not make arbitrary writes exactly once.
Stream transformations can filter or project event fields before ingestion. It does not show that a replay of the same batch ID does not duplicate target business records.
KQL binning groups events into fixed time buckets. It does not show that a replay of the same batch ID does not duplicate target business records.
The stream must have an active compatible destination for events to be materialized there. It does not show that a replay of the same batch ID does not duplicate target business records.
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