A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

• A. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
• B. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
• C. Use apply_changes without CDF and filter rows where _soft_deleted is true.
• D. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.

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The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

• A. Cluster creation allowed. "Can Restart" privileges on the required cluster
• B. Cluster creation allowed. "Can Attach To" privileges on the required cluster
• C. "Can Manage" privileges on the required cluster
• D. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
• E. "Can Restart" privileges on the required cluster

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A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

• A. slidingWindow("event_time", "10 minutes")
• B. awaitArrival("event_time", "10 minutes")
• C. delayWrite("event_time", "10 minutes")
• D. await("event_time + `10 minutes'")
• E. withWatermark("event_time", "10 minutes")

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The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

• A. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
• B. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
• C. The source query failed to update properly for three consecutive minutes and then restarted
• D. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
• E. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query

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Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

• A. spark.sql.autoBroadcastJoinThreshold
• B. spark.sql.files.openCostInBytes
• C. spark.sql.files.maxPartitionBytes
• D. spark.sql.adaptive.coalescePartitions.minPartitionNum
• E. spark.sql.adaptive.advisoryPartitionSizeInBytes

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