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A shared Fabric backlog includes two mandatory controls. First: A Spark job creates 5,000 partitions for a small 500 MB input and spends more time scheduling than computing. Second: A warehouse T-SQL query selects all columns from a wide fact table though reporting uses only three numeric measures. Which two-part implementation satisfies them together?
Step 1 resolves requirement A: Too many tiny tasks increase scheduling overhead and file output fragmentation. Step 2 resolves requirement B: Column pruning and selective filters reduce unnecessary data scan and transfer. Both acceptance tests are required: the same workload completes faster with a reasonable number of partitions; and the revised query returns identical measures with lower bytes processed.
Too many tiny tasks increase scheduling overhead and file output fragmentation. Column pruning and selective filters reduce unnecessary data scan and transfer. The proposed steps (Increase supported parallel Copy/ForEach concurrency within source quotas and capacity limits; run supported Delta OPTIMIZE compaction for the affected table after evaluating workload impact) address other capabilities instead of this required pair.
Column pruning and selective filters reduce unnecessary data scan and transfer. The other proposed action (Increase supported parallel Copy/ForEach concurrency within source quotas and capacity limits) does not implement the first control. Too many tiny tasks increase scheduling overhead and file output fragmentation.
Too many tiny tasks increase scheduling overhead and file output fragmentation. The other proposed action (run supported Delta OPTIMIZE compaction for the affected table after evaluating workload impact) addresses a different operation. Column pruning and selective filters reduce unnecessary data scan and transfer.
A data engineer must validate the completed action: Use an appropriate streaming path such as Eventstream to Eventhouse, rather than a daily Copy job. It targets this failure: Telemetry arrives continuously from IoT devices, and operations needs near-real-time anomaly analysis. Which test outcome should be accepted?
Streaming architectures ingest and query events with low delay compared with scheduled batch loads.
Event time supports domain analysis and processing time supports operational freshness diagnosis. It does not show that a representative sensor event is queryable within the accepted delay target.
Checkpoint commit order is essential for recoverability and at-least-once source reads. It does not show that a representative sensor event is queryable within the accepted delay target.
A file-ready boundary prevents reading partially delivered source data. It does not show that a representative sensor event is queryable within the accepted delay target.
A Fabric release must resolve two independent technical requirements. Requirement A: A Lakehouse table is repeatedly rewritten in full even when only 1% of records change. Requirement B: A warehouse query repeatedly filters and joins on columns with poor estimates despite a correct table schema. Select TWO actions that directly satisfy both requirements.
Column statistics support cost-based query planning without forcing a specific data distribution. Confirm 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. Confirm that updated rows are correct and the write volume falls compared with full refresh.
Broadcasting a small table can avoid a costly large-side shuffle.
Skewed partitions cause straggler tasks and spill even with high total cluster capacity.
The operations team has applied: Review and configure relevant workspace OneLake settings and data security controls rather than notebook code alone. Its purpose was: Operators need to set common OneLake workspace behavior and data access safeguards before onboarding sensitive pipelines. Which of the following provides the strongest direct evidence of success?
Workspace-level configuration governs data access and connectivity across multiple items.
Environment drafts and published settings must be distinguished; existing sessions can retain earlier configuration. It does not show that a newly created item follows the approved workspace access and outbound policy.
Autoscale adjusts cluster nodes and dynamic allocation adjusts executor resources based on workload demand. It does not show that a newly created item follows the approved workspace access and outbound policy.
Version-controlled runtime and dependency changes prevent surprise job differences. It does not show that a newly created item follows the approved workspace access and outbound policy.
The operations team has applied: Constrain ForEach activity concurrency and configure a controlled retry budget. Its purpose was: A source API has a strict rate limit, and unbounded parallel ForEach copies cause frequent HTTP 429 responses. Which of the following provides the strongest direct evidence of success?
Controlled parallelism protects external API quotas without reducing correctness of the load.
Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code. It does not show that a burst test stays within the supported rate and all records arrive once.
Spark code supports programmable distributed transforms and Delta upsert patterns. It does not show that a burst test stays within the supported rate and all records arrive once.
Parameterized notebook execution separates business run context from transformation logic. It does not show that a burst test stays within the supported rate and all records arrive once.
The Fabric engineering team must address this requirement: A business analyst needs Power Query merge and column cleanup without writing PySpark. Which implementation meets the requirement and provides this acceptance evidence: the dataflow outputs a correctly typed joined table after refresh?
Power Query-based dataflow transformations suit low-code batch shaping. Verify that the dataflow outputs a correctly typed joined table after refresh.
Mirroring replicates supported source changes into Fabric with managed synchronization. However, this alone does not show that the dataflow outputs a correctly typed joined table after refresh.
KQL grouping can build time-series metrics directly from event records. However, this alone does not show that the dataflow outputs a correctly typed joined table after refresh.
Warehouse dataflow destinations can depend on an intermediate staging path. However, this alone does not show that the dataflow outputs a correctly typed joined table after refresh.
An auditor reviews the applied Fabric control: Detect skew and use supported skew handling or repartitioning of the hot keys. It was selected to address: A Spark job's largest customer accounts put nearly all join records in one partition. Which evidence demonstrates the intended result?
Skewed partitions cause straggler tasks and spill even with high total cluster capacity.
Fabric Warehouse manual statistics support is narrower than general SQL Server feature assumptions. It does not show that task durations become more balanced with unchanged join results.
Early time restriction and column projection reduce Eventhouse query work. It does not show that task durations become more balanced with unchanged join results.
Incremental updates reduce unnecessary reads and rewrites when source change data is available. It does not show that task durations become more balanced with unchanged join results.
A Fabric data engineering release must address both of these independent requirements: (1) A data engineer wants to reclaim obsolete Delta files immediately after a write, ignoring active readers and time travel. (2) An Eventhouse receives thousands of tiny queued ingestion files and spends more resources on ingestion overhead than on useful analysis. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: VACUUM removes files no longer needed beyond the retention threshold; aggressive cleanup can break readers or history. Step 2 resolves requirement B: Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay. Both acceptance tests are required: storage cleanup retains files needed by approved concurrent readers and historical queries; and a controlled benchmark shows lower ingestion overhead while events remain queryable within the agreed delay.
Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay. The other proposed action (Detect skew and use supported skew handling or repartitioning of the hot keys) does not implement the first control. VACUUM removes files no longer needed beyond the retention threshold; aggressive cleanup can break readers or history.
VACUUM removes files no longer needed beyond the retention threshold; aggressive cleanup can break readers or history. The other proposed action (use Query Insights history and long-running/frequently-run query views to prioritize tuning) addresses a different operation. Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay.
VACUUM removes files no longer needed beyond the retention threshold; aggressive cleanup can break readers or history. Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay. The proposed steps (Detect skew and use supported skew handling or repartitioning of the hot keys; use Query Insights history and long-running/frequently-run query views to prioritize tuning) address other capabilities instead of this required pair.
A Fabric data engineering release must address both of these independent requirements: (1) A deployment pipeline promotes a notebook that references a development lakehouse, and production jobs still target development data. (2) A notebook is pinned to a specific runtime but its Git commit omits the required linked environment item. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: Promotion of definitions does not imply every environment-specific connection is automatically safe. Step 2 resolves requirement B: Notebook definitions can depend on environment-level libraries and Spark versions. Both acceptance tests are required: a production test run reads only the approved production lakehouse; and the promoted notebook starts with its required package and runtime configuration.
Promotion of definitions does not imply every environment-specific connection is automatically safe. The other proposed action (use supported Git branching and pull request review before merging item-definition changes) addresses a different operation. Notebook definitions can depend on environment-level libraries and Spark versions.
Notebook definitions can depend on environment-level libraries and Spark versions. The other proposed action (Check the supported item matrix and use an alternate governed release path for unsupported items) 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. Notebook definitions can depend on environment-level libraries and Spark versions. The proposed steps (Check the supported item matrix and use an alternate governed release path for unsupported items; use supported Git branching and pull request review before merging item-definition changes) address other capabilities instead of this required pair.
After a production change, the runbook records: Create a governed denormalized projection from fact and dimension source tables. The previous problem was: A business asks to denormalize small product attributes into a gold sales table for faster analyst queries. What test result confirms effective remediation?
Denormalization trades duplication for simpler downstream analytic reads.
Warehouse is optimized for supported SQL data engineering and analytics operations. It does not show that the gold table includes valid product labels and agrees with dimensional source counts.
Mirroring depends on source-specific permissions and configuration, not only workspace Reader rights. It does not show that the gold table includes valid product labels and agrees with dimensional source counts.
Deterministic precedence avoids unpredictable retained records. It does not show that the gold table includes valid product labels and agrees with dimensional source counts.
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.
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