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A retailer wants SQL analysts to query sales data without provisioning and administering a long-lived Spark cluster.
Which implementation decision best addresses this requirement?
SQL warehouses are purpose-built SQL compute and can be configured for serverless operation while separating query execution from cluster administration. The selected implementation is the one directly aligned with the constraints in the scenario.
General interactive compute supports exploratory Python and Spark development; a SQL warehouse is not the default tool for arbitrary notebook PySpark dependencies.
Delta Lake provides transactional table semantics on cloud object storage while supporting diverse compute engines and workloads.
Different Databricks compute types are optimized for query serving and flexible Spark development, limiting resource contention.
A retailer wants SQL analysts to query sales data without provisioning and administering a long-lived Spark cluster.
Which acceptance result most directly demonstrates that the intended solution works?
SQL warehouses are purpose-built SQL compute and can be configured for serverless operation while separating query execution from cluster administration.
Scientists execute the PySpark notebook with the approved libraries while jobs remain isolated from the analyst warehouse. That finding does not establish that analysts run the required SQL against Unity Catalog tables through a warehouse and its cost can be attributed to the team.
Two independent authorized workloads read a consistent Delta table state after a committed write. That finding does not establish that analysts run the required SQL against Unity Catalog tables through a warehouse and its cost can be attributed to the team.
Dashboard latency remains stable during a developer's large exploratory notebook job. That finding does not establish that analysts run the required SQL against Unity Catalog tables through a warehouse and its cost can be attributed to the team.
A research group runs interactive PySpark notebooks requiring custom runtime dependencies, and each scientist needs a flexible development environment.
Which implementation decision best addresses this requirement?
General interactive compute supports exploratory Python and Spark development; a SQL warehouse is not the default tool for arbitrary notebook PySpark dependencies. The selected implementation is the one directly aligned with the constraints in the scenario.
Task compute is lifecycle-bound to production jobs and avoids idle all-purpose development capacity.
Unity Catalog centralizes discoverability, governance and permissions independently of a specific notebook or workstation.
Runtime alone does not capture cost differences among warehouses, cluster sizes and serverless execution modes.
A research group runs interactive PySpark notebooks requiring custom runtime dependencies, and each scientist needs a flexible development environment.
Which acceptance result most directly demonstrates that the intended solution works?
General interactive compute supports exploratory Python and Spark development; a SQL warehouse is not the default tool for arbitrary notebook PySpark dependencies.
The nightly task starts at its trigger, finishes successfully, and incurs no unnecessary always-on interactive compute time. That finding does not establish that scientists execute the PySpark notebook with the approved libraries while jobs remain isolated from the analyst warehouse.
An authorized group discovers and queries an approved table while an unrelated group is denied data access. That finding does not establish that scientists execute the PySpark notebook with the approved libraries while jobs remain isolated from the analyst warehouse.
A usage report separates costs by workload owner and compute usage for the same reporting period. That finding does not establish that scientists execute the PySpark notebook with the approved libraries while jobs remain isolated from the analyst warehouse.
A finance platform wants a scheduled ETL task to run unattended without leaving an interactive development cluster powered on.
Which implementation decision best addresses this requirement?
Task compute is lifecycle-bound to production jobs and avoids idle all-purpose development capacity. The selected implementation is the one directly aligned with the constraints in the scenario.
Delta Lake provides transactional table semantics on cloud object storage while supporting diverse compute engines and workloads.
The Delta transaction log tracks consistent table versions and coordinates transactional updates rather than relying on ad hoc file replacement.
ACID-oriented Delta transactions help readers see coherent committed versions while ingestion continues.
A finance platform wants a scheduled ETL task to run unattended without leaving an interactive development cluster powered on.
Which acceptance result most directly demonstrates that the intended solution works?
Task compute is lifecycle-bound to production jobs and avoids idle all-purpose development capacity.
Two independent authorized workloads read a consistent Delta table state after a committed write. That finding does not establish that the nightly task starts at its trigger, finishes successfully, and incurs no unnecessary always-on interactive compute time.
A reader sees a committed snapshot and not a partially applied batch of underlying file changes. That finding does not establish that the nightly task starts at its trigger, finishes successfully, and incurs no unnecessary always-on interactive compute time.
A long-running reader sees a consistent data snapshot while a new batch commits without partial rows. That finding does not establish that the nightly task starts at its trigger, finishes successfully, and incurs no unnecessary always-on interactive compute time.
A business team asks why a Delta table can serve both downstream SQL reporting and batch data engineering without creating two unrelated data copies.
Which implementation decision best addresses this requirement?
Delta Lake provides transactional table semantics on cloud object storage while supporting diverse compute engines and workloads. The selected implementation is the one directly aligned with the constraints in the scenario.
Unity Catalog centralizes discoverability, governance and permissions independently of a specific notebook or workstation.
Different Databricks compute types are optimized for query serving and flexible Spark development, limiting resource contention.
Workspace assets and data-governance objects have different scopes; notebook sharing is not a substitute for object privileges.
A business team asks why a Delta table can serve both downstream SQL reporting and batch data engineering without creating two unrelated data copies.
Which acceptance result most directly demonstrates that the intended solution works?
Delta Lake provides transactional table semantics on cloud object storage while supporting diverse compute engines and workloads.
An authorized group discovers and queries an approved table while an unrelated group is denied data access. That finding does not establish that two independent authorized workloads read a consistent Delta table state after a committed write.
Dashboard latency remains stable during a developer's large exploratory notebook job. That finding does not establish that two independent authorized workloads read a consistent Delta table state after a committed write.
Users with notebook access but no table privileges still cannot query protected datasets. That finding does not establish that two independent authorized workloads read a consistent Delta table state after a committed write.
Data scientists want to find approved customer datasets across workspaces and control who can read the sensitive tables.
Which implementation decision best addresses this requirement?
Unity Catalog centralizes discoverability, governance and permissions independently of a specific notebook or workstation. The selected implementation is the one directly aligned with the constraints in the scenario.
The Delta transaction log tracks consistent table versions and coordinates transactional updates rather than relying on ad hoc file replacement.
Runtime alone does not capture cost differences among warehouses, cluster sizes and serverless execution modes.
Reading governed data and modifying governed data are distinct operations enforced by Unity Catalog permissions.
Data scientists want to find approved customer datasets across workspaces and control who can read the sensitive tables.
Which acceptance result most directly demonstrates that the intended solution works?
Unity Catalog centralizes discoverability, governance and permissions independently of a specific notebook or workstation.
A reader sees a committed snapshot and not a partially applied batch of underlying file changes. That finding does not establish that an authorized group discovers and queries an approved table while an unrelated group is denied data access.
A usage report separates costs by workload owner and compute usage for the same reporting period. That finding does not establish that an authorized group discovers and queries an approved table while an unrelated group is denied data access.
A reader can run SELECT but cannot insert rows or change table ownership. That finding does not establish that an authorized group discovers and queries an approved table while an unrelated group is denied data access.
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