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A data engineer must validate the completed action: Use supported CREATE or UPDATE STATISTICS on relevant filter/join columns as needed. It targets this failure: A warehouse query repeatedly filters and joins on columns with poor estimates despite a correct table schema. Which test outcome should be accepted?
Column statistics support cost-based query planning without forcing a specific data distribution.
Early source-side filtering reduces data that the dataflow engine must move and transform. It does not show that the plan's row estimates and elapsed time improve after appropriate statistics maintenance.
Incremental updates reduce unnecessary reads and rewrites when source change data is available. It does not show that the plan's row estimates and elapsed time improve after appropriate statistics maintenance.
Data movement bottlenecks can be independent of Spark compute. It does not show that the plan's row estimates and elapsed time improve after appropriate statistics maintenance.
A data engineer must validate the completed action: Put quality gates and approval checks in the supported CI/CD workflow before the production deployment action. It targets this failure: A pipeline update must run only after an approval and passing preproduction tests. Which test outcome should be accepted?
Version control alone does not enforce release quality or change authorization.
A Git merge does not necessarily update every connected workspace or deployment stage. It does not show that a deliberately failing test prevents the production stage from changing.
A linked branch does not automatically mean every local workspace change has been committed. It does not show that a deliberately failing test prevents the production stage from changing.
Database project source control can make targeted DDL changes auditable. It does not show that a deliberately failing test prevents the production stage from changing.
The Fabric engineering team must address this requirement: A KQL table contains service events and a dashboard needs hourly failure rates by application. Which implementation meets the requirement and provides this acceptance evidence: a controlled event set produces the expected hourly failure counts?
KQL grouping can build time-series metrics directly from event records. Verify that a controlled event set produces the expected hourly failure counts.
Shortcuts can expose external data without per-refresh duplication. However, this alone does not show that a controlled event set produces the expected hourly failure counts.
Ingestion validation separates bad source rows from trustworthy analytic data. However, this alone does not show that a controlled event set produces the expected hourly failure counts.
Shortcuts present source data under OneLake paths without physically duplicating the source files. However, this alone does not show that a controlled event set produces the expected hourly failure counts.
The operations team has applied: Attach notification to the success path of the final validation activity, with failure reporting separately. Its purpose was: A nightly pipeline must not send a completion notice if the data quality notebook fails. Which of the following provides the strongest direct evidence of success?
Dependency outcomes prevent false operational success claims.
Query transformations and data destination settings are separate deployment concerns. It does not show that the success notice appears only after validation and the failure notice carries the error when validation fails.
Warehouse destinations in Dataflow Gen2 can require staging before the destination write. It does not show that the success notice appears only after validation and the failure notice carries the error when validation fails.
Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code. It does not show that the success notice appears only after validation and the failure notice carries the error when validation fails.
A data engineer must validate the completed action: Apply explicit type conversion with an error-handling or quarantine branch before writing the numeric destination. It targets this failure: A Dataflow Gen2 refresh fails with a Power Query conversion error because an amount column now contains the string N/A in several source rows. Which test outcome should be accepted?
Invalid source text causes a deterministic type-conversion failure that extra retries or staging changes cannot repair.
Schema drift can break a streaming destination even when the source remains connected. It does not show that rows with valid amounts load as numbers and invalid source values are captured for remediation.
Stream routing can discard records during transformations before they reach a sink. It does not show that rows with valid amounts load as numbers and invalid source values are captured for remediation.
SQL dialect support differs by Fabric engine and operation. It does not show that rows with valid amounts load as numbers and invalid source values are captured for remediation.
A shared Fabric backlog includes two mandatory controls. First: A KQL query must identify missing hourly intervals in a telemetry series rather than just list hours containing events. Second: A producer sends nested events that require field extraction before analytics users can query key dimensions. Which two-part implementation satisfies them together?
Step 1 resolves requirement A: Make-series generates regular time bins, including configured defaults for empty periods. Step 2 resolves requirement B: Schema mapping makes raw event properties accessible as KQL columns. Both acceptance tests are required: the output contains the missing hour as a zero or null according to design; and an event with nested attributes produces queryable correctly typed keys.
Make-series generates regular time bins, including configured defaults for empty periods. The other proposed action (assign an independent checkpoint directory to each query) addresses a different operation. Schema mapping makes raw event properties accessible as KQL columns.
Schema mapping makes raw event properties accessible as KQL columns. The other proposed action (Make foreachBatch writes idempotent using stable event keys, batch IDs or transactional merging) does not implement the first control. Make-series generates regular time bins, including configured defaults for empty periods.
Make-series generates regular time bins, including configured defaults for empty periods. Schema mapping makes raw event properties accessible as KQL columns. The proposed steps (Make foreachBatch writes idempotent using stable event keys, batch IDs or transactional merging; assign an independent checkpoint directory to each query) address other capabilities instead of this required pair.
After a production change, the runbook records: Use a supported transformation tool to expand nested records and map types before loading the Warehouse. The previous problem was: An external source has nested JSON structures and the curated warehouse requires flat analytic columns. What test result confirms effective remediation?
Flattening nested structures creates the relational schema required by T-SQL consumers.
A shortcut is a reference to data, not an independently secured copy with unlimited access. It does not show that the nested sample yields the expected flattened columns and preserves its parent identifier.
Warehouse is optimized for supported SQL data engineering and analytics operations. It does not show that the nested sample yields the expected flattened columns and preserves its parent identifier.
Mirroring replicates supported source changes into Fabric with managed synchronization. It does not show that the nested sample yields the expected flattened columns and preserves its parent identifier.
A Fabric release must resolve two independent technical requirements. Requirement A: A data pipeline has to branch based on an extracted row-count threshold before loading a warehouse. Requirement B: A Dataflow Gen2 refresh must land transformed rows into a Warehouse and fails because its staging prerequisites were removed. Select TWO actions that directly satisfy both requirements.
Conditional orchestration can route execution based on prior activity outputs. Confirm that a low-row-count test takes the exception path and a valid count follows the load branch.
Warehouse destinations in Dataflow Gen2 can require staging before the destination write. Confirm that the refreshed dataflow loads into the target Warehouse with no staging validation warning.
Spark code supports programmable distributed transforms and Delta upsert patterns.
Parameterized notebook execution separates business run context from transformation logic.
A Fabric release must resolve two independent technical requirements. Requirement A: The organization wants Finance domain contributors to find a certified dataset without making it publicly writable. Requirement B: A workspace default environment upgrade changed notebook startup behavior unexpectedly. Select TWO actions that directly satisfy both requirements.
Version-controlled runtime and dependency changes prevent surprise job differences. Confirm that a regression notebook reproduces approved behavior on the promoted environment.
Domain metadata and endorsement communicate ownership and trust, not write grants. Confirm that the dataset appears under the domain and unauthorized edits remain denied.
Airflow tasks rely on configured connections and runtime settings independent of a Fabric pipeline definition.
Outbound protection restricts data egress paths independently of ordinary item-level read permissions.
A Fabric engineer has implemented the following change: Consider a broadcast join for the small validated dimension if memory limits permit. The original requirement was: A PySpark join spills heavily because a large fact table joins a tiny static lookup with a full shuffle. Which observation best validates that the change works?
Broadcasting a small table can avoid a costly large-side shuffle.
Controlled concurrency can reduce end-to-end pipeline runtime without saturating dependencies. It does not show that the physical plan shows the intended join strategy and job runtime improves.
Column statistics support cost-based query planning without forcing a specific data distribution. It does not show that the physical plan shows the intended join strategy and job runtime improves.
File compaction reduces small-file overhead and can improve read efficiency. It does not show that the physical plan shows the intended join strategy and job runtime improves.
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.
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