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A production analytics workflow has this requirement: A new Dataflow Gen2 refresh appears to succeed but row totals double because the destination is in Append mode. What is the most appropriate change if success must be shown by this test: two identical refreshes produce stable logical row counts?
Destination write mode defines whether repeat refreshes accumulate or replace records. Verify that two identical refreshes produce stable logical row counts.
Schema drift can break a streaming destination even when the source remains connected. However, this alone does not show that two identical refreshes produce stable logical row counts.
Shortcut path references are not automatically rewritten when source folders are relocated. However, this alone does not show that two identical refreshes produce stable logical row counts.
Collect moves data to driver memory and can exhaust it irrespective of executor capacity. However, this alone does not show that two identical refreshes produce stable logical row counts.
The reviewer will approve this deployment only when it fixes both issues: (a) A Power BI semantic model shows stale metrics after data ingestion succeeded. (b) A dashboard must show failed source refreshes across lakehouses and warehouses without granting operators write access. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: Upstream data arrival is separate from semantic model refresh completion. Step 2 resolves requirement B: Monitoring visibility can be separated from authoring or item modification privileges. Both acceptance tests are required: a successful model refresh timestamp follows the completed ingest and report values update; and an observer sees relevant failures but cannot change pipeline definitions.
Monitoring visibility can be separated from authoring or item modification privileges. The other proposed action (Open the Dataflow Gen2 refresh history and examine query/destination errors and row activity) does not implement the first control. Upstream data arrival is separate from semantic model refresh completion.
Upstream data arrival is separate from semantic model refresh completion. The other proposed action (open the notebook/Spark application run details and relevant driver or executor logs) addresses a different operation. Monitoring visibility can be separated from authoring or item modification privileges.
Upstream data arrival is separate from semantic model refresh completion. Monitoring visibility can be separated from authoring or item modification privileges. The proposed steps (Open the Dataflow Gen2 refresh history and examine query/destination errors and row activity; open the notebook/Spark application run details and relevant driver or executor logs) address other capabilities instead of this required pair.
A Microsoft Fabric project must complete both tasks before signoff. Task 1: A production workspace must disallow notebook authors from freely overriding pool sizes because cost controls depend on approved defaults. Task 2: Teams want separate ownership of marketing and finance data while using the same Fabric tenant. Choose exactly TWO controls that collectively meet the requirements.
Business domains organize content, while workspaces and OneLake roles govern operational access. Confirm that each domain inventory shows its own workspaces and access tests respect role boundaries.
Workspace governance can enforce shared Spark compute settings instead of permitting arbitrary environment overrides. Confirm that a contributor cannot change the blocked environment compute configuration.
Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements.
Workspace-level configuration governs data access and connectivity across multiple items.
The data platform team needs a narrowly scoped fix: Daily notebook outputs should generate an alert when row counts drop outside an accepted band. Which approach satisfies the documented test that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not?
Data quality anomalies may not appear as compute failures unless explicitly measured. Verify that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
Query history exposes SQL execution trends distinct from copy/load duration. However, this alone does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
Run provenance identifies the environment that produced a dataset. However, this alone does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
Event-to-query latency differs from service-only ingest processing time. However, this alone does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
The data platform team needs a narrowly scoped fix: One high-memory Spark stage consistently exceeds the capacity limit although retries succeed only sporadically. Which approach satisfies the documented test that a controlled concurrency test runs jobs within the approved capacity envelope?
Capacity exhaustion differs from transient faults and requires resource or concurrency management. Verify that a controlled concurrency test runs jobs within the approved capacity envelope.
Workspace-level configuration governs data access and connectivity across multiple items. However, this alone does not show that a controlled concurrency test runs jobs within the approved capacity envelope.
Domain assignment supports discoverability and governance but is not the row or item authorization boundary. However, this alone does not show that a controlled concurrency test runs jobs within the approved capacity envelope.
Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements. However, this alone does not show that a controlled concurrency test runs jobs within the approved capacity envelope.
The project has two blockers. Blocker A: A workspace's data engineers must control the default Apache Airflow integration behavior without editing each existing DAG. Blocker B: A Spark session fails when a custom library is installed in a notebook cell but not available to scheduled jobs. What pair of actions resolves both blockers?
Step 1 resolves requirement A: Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements. Step 2 resolves requirement B: A reusable environment is more reliable than relying on interactive-only session mutations. Both acceptance tests are required: a scheduled DAG runs using the approved workspace integration settings; and the scheduled Spark job imports the same dependency as the interactive notebook.
Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements. A reusable environment is more reliable than relying on interactive-only session mutations. The proposed steps (Assign the workspace to the approved Fabric domain through domain/workspace settings; add the supported narrowly scoped data-connection rule or managed private endpoint for the approved target) address other capabilities instead of this required pair.
Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements. The other proposed action (add the supported narrowly scoped data-connection rule or managed private endpoint for the approved target) addresses a different operation. A reusable environment is more reliable than relying on interactive-only session mutations.
A reusable environment is more reliable than relying on interactive-only session mutations. The other proposed action (Assign the workspace to the approved Fabric domain through domain/workspace settings) does not implement the first control. Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements.
A Fabric data engineering release must address both of these independent requirements: (1) Fabric notebooks are edited directly in production, and the team cannot identify which Git commit introduced a failing transformation. (2) A database project contains a table definition change that would unintentionally drop production data. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: Fabric Git integration versions item definitions so changes can be reviewed and traced. Step 2 resolves requirement B: Database schema deployment can have destructive consequences even when a source diff is syntactically valid. Both acceptance tests are required: the problematic notebook definition can be mapped to and reverted from a recorded commit; and the deployment test preserves existing production rows and rejects unapproved drops.
Fabric Git integration versions item definitions so changes can be reviewed and traced. Database schema deployment can have destructive consequences even when a source diff is syntactically valid. The proposed steps (Assign a distinct, approved workspace to each stage and verify stage-item bindings; configure a supported Fabric deployment pipeline with development, test and production workspaces) address other capabilities instead of this required pair.
Database schema deployment can have destructive consequences even when a source diff is syntactically valid. The other proposed action (Assign a distinct, approved workspace to each stage and verify stage-item bindings) does not implement the first control. Fabric Git integration versions item definitions so changes can be reviewed and traced.
Fabric Git integration versions item definitions so changes can be reviewed and traced. The other proposed action (configure a supported Fabric deployment pipeline with development, test and production workspaces) addresses a different operation. Database schema deployment can have destructive consequences even when a source diff is syntactically valid.
A Fabric engineer has implemented the following change: Use a five-minute tumbling window on the event timestamp. The original requirement was: A streaming query needs a nonoverlapping five-minute aggregate that resets at each fixed boundary. Which observation best validates that the change works?
Tumbling windows partition time into fixed, nonoverlapping intervals.
Separating invalid records from typed valid rows allows streaming jobs to continue safely. It does not show that events at two adjacent intervals appear in distinct five-minute aggregates.
Materialized aggregations can precompute repeated summaries to reduce query cost. It does not show that events at two adjacent intervals appear in distinct five-minute aggregates.
A too-short watermark can exclude valid late events; a longer delay retains more state. It does not show that events at two adjacent intervals appear in distinct five-minute aggregates.
An auditor reviews the applied Fabric control: Compute and publish validation metrics and attach an alert to a threshold breach. It was selected to address: Daily notebook outputs should generate an alert when row counts drop outside an accepted band. Which evidence demonstrates the intended result?
Data quality anomalies may not appear as compute failures unless explicitly measured.
Run provenance identifies the environment that produced a dataset. It does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
Source activity does not prove a downstream destination is consuming messages. It does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
Upstream data arrival is separate from semantic model refresh completion. It does not show that a deliberately truncated batch triggers an anomaly alert while the prior good batch does not.
A Fabric data engineer receives a change request: Fabric notebooks are edited directly in production, and the team cannot identify which Git commit introduced a failing transformation. Which action best produces this measurable result: the problematic notebook definition can be mapped to and reverted from a recorded commit?
Fabric Git integration versions item definitions so changes can be reviewed and traced. Verify that the problematic notebook definition can be mapped to and reverted from a recorded commit.
Version control alone does not enforce release quality or change authorization. However, this alone does not show that the problematic notebook definition can be mapped to and reverted from a recorded commit.
Git sync and environment deployment are different workflows with different source-of-truth expectations. However, this alone does not show that the problematic notebook definition can be mapped to and reverted from a recorded commit.
Stage separation isolates release scopes so testing does not modify the production workspace. However, this alone does not show that the problematic notebook definition can be mapped to and reverted from a recorded commit.
An auditor reviews the applied Fabric control: Use the applicable Microsoft Fabric/Microsoft Purview audit log search with principal, time and operation filters. It was selected to address: Auditors need to determine who changed an artifact or accessed supported Fabric resources during a specified interval. Which evidence demonstrates the intended result?
Supported Fabric audit events expose administrative or item activities without being the same as pipeline data logs.
Promotion is a discoverability signal; certification requires an authorized organizational review process. It 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. It does not show that the audit query returns the matching activity with actor and timestamp.
Combining row and column filters in unsupported separate roles can cause query errors. It does not show that the audit query returns the matching activity with actor and timestamp.
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