Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)

B and D are correct.

Explanation: A Max duration more than 50% above the 75th percentile indicates data skew, and the repeated customer ID is a common cause. AQE dynamically handles skewed joins, and salting distributes the skewed key across partitions. Spill often accompanies skew, so addressing skew can also resolve the spill.

A is incorrect: Each call to repartition() triggers a full shuffle. Unnecessary repartitioning increases data movement.

C is incorrect: Spark cache requires explicit management. Cached data that you do not unpersist consumes memory that Spark needs for processing, which can worsen memory pressure.

D is correct.

Explanation: Repair run re-runs only the unsuccessful tasks and their dependents. This saves time and compute compared with restarting the whole job. Repair is available for jobs with two or more tasks, and this job has five.

A is incorrect: Maximum concurrent runs addresses runs that queue or are skipped behind other runs. It does not resolve a task failure, and starting from the first task re-runs successful tasks.

B is incorrect: Restart run applies to continuous jobs that are in an exponential backoff state. It cancels the active run and starts a new run.

C is incorrect: A full run repeats tasks that already succeeded. This uses more time and compute than a repair run.

A is correct.

Explanation: All done runs the task after all upstream tasks finish, regardless of outcome. This fits a cleanup task that must release temporary resources in every case.

B is incorrect: All succeeded runs the task only when every upstream task completes successfully. Cleanup would not run after a failure.

C is incorrect: At least one failed runs the task only when an upstream task fails. Cleanup would not run when all ingestion tasks succeed.

D is incorrect: None failed runs the task when no upstream task failed, even if some were skipped. Cleanup would not run after a failure.

D is correct.

Explanation: Lakeflow pipelines fit standardized ETL patterns such as reading from cloud storage, schema evolution, and slowly changing dimensions. They provide expectations that track violations and can halt processing, and they provide lineage tracking and monitoring without additional configuration. They are also SQL-friendly, which lowers the barrier for the team.

A is incorrect: Notebooks require custom implementation for error handling and manual validation code. This adds code to write and maintain and does not give built-in expectations.

B is incorrect: Notebooks require external tooling for lineage tracking. Lineage tracking is automatic in Lakeflow pipelines.

C is incorrect: Cell-by-cell iteration is useful for prototyping and exploration. It is not a reason to choose notebooks for a repeatable production workload with standard patterns.

B is correct.

Explanation: Ingestion should preserve data with minimal transformation, which corresponds to the bronze layer. Cleaning operations such as schema enforcement and deduplication belong in a separate stage that corresponds to the silver layer. This separation keeps the raw data available for auditing and reprocessing, and it gives later transformations validated data to build on.

A is incorrect: Cleaning during ingestion and discarding the original records removes the raw state, so you cannot reprocess data or audit it later.

C is incorrect: Cleaning in the load stage lets quality issues propagate through ingestion and transformation first. Data quality issues should be caught early.

D is incorrect: The serving layer provides access patterns, performance, and access control for consumers. Using it to fix data quality problems leaves the underlying tables unvalidated.

B is correct.

Explanation: Tags propagate from clusters and workspaces to billing records. You can query system.billing.usage and filter or group by custom_tags to see DBU usage for a project. Apply the tags consistently from the start.

A is incorrect: The Metrics tab shows hardware, Spark, and GPU metrics. It does not provide billing data that you can attribute to a project.

C is incorrect: Budgets track spending and send email notifications. Creating and managing budgets requires account admin permissions, and workspace admins typically cannot create them.

D is incorrect: You cannot add tags retroactively to historical usage. Usage that occurs before you apply the tags cannot be attributed by tag.

C is correct.

Explanation: A streaming table is a Delta table for append-only processing and handles each input record once, so it fits ingestion of new files from cloud storage. A materialized view handles aggregations and complex joins and stays synchronized when upstream data changes, so it fits the join and regional sums.

A is incorrect: Materialized views are intended for transformations such as aggregations and joins, not for append-only ingestion that processes each record once.

B is incorrect: Streaming tables are for append-only data. They are not the right choice for aggregations and joins on dimensions that can change.

D is incorrect: This reverses the roles. The append-only ingestion fits a streaming table, and the aggregation with a join on changing data fits a materialized view.

A is correct.

Explanation: A red driver node with blue (idle) workers indicates that the driver is overloaded. A larger driver instance type addresses that constraint.

B is incorrect: The workers are idle, so more workers add capacity where it is not needed. The bottleneck is the driver.

C is incorrect: Restarting a cluster terminates running jobs and resets the Spark UI history. It removes diagnostic information and does not address the overloaded driver.

D is incorrect: Auto-termination shuts down idle clusters to control cost. It does not change driver capacity or fix slow execution.

C is correct.

Explanation: Log streaming sends diagnostic data to Azure Log Analytics, where events from multiple workspaces can be queried in one place. The DatabricksJobs table holds job runs and failures. You can create an alert rule from a KQL query and use an action group to define who is notified. Diagnostic logs require the Azure Databricks Premium plan.

A is incorrect: The system.access.audit table is recommended for querying audit logs within Azure Databricks. Log streaming is the preferred approach for cross-service centralized monitoring and KQL-based alerting. Separate queries per workspace also do not give one place for the data.

B is incorrect: DatabricksClusters records cluster events such as creation, termination, and resizing. It does not record job failures.

D is incorrect: The Spark UI shows execution details for a single cluster. It does not centralize events across workspaces and does not send notifications.

B and C are correct.

Explanation: Transient failures such as network timeouts often resolve themselves, so a retry policy with a retry count and a retry interval lets the task run again after the system recovers. Task-level notifications fire on each retry attempt, so selecting Mute notifications until the last retry reduces alerts and still notifies the team if the final attempt fails.

A is incorrect: The All done dependency condition controls when a task runs relative to upstream tasks. It does not retry a failed task or reduce notifications.

D is incorrect: ON VIOLATION FAIL UPDATE applies to data quality expectations in Lakeflow pipelines and stops a flow when a record violates a constraint. It does not handle transient network timeouts or notification volume.