A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for
three consecutive days.
Ensure that notifications are not spammed during sustained issues.

• A. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
• B. WITH daily_avg AS (
  SELECT DATE_TRUNC('DAY', window.end) AS day,
  AVG(percent_null) AS avg_null
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  GROUP BY DATE_TRUNC('DAY', window.end)
  )
  SELECT day, avg_null
  FROM daily_avg
  ORDER BY day DESC
  LIMIT 3
  Alert Condition: ALL avg_null > 5 for the latest 3 rows
  Notification Frequency: Just once
• C. SELECT percent_null
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
  Alert Condition: percent_null > 5
  Notification Frequency: At most every 24 hours
• D. SELECT AVG(percent_null) AS daily_avg
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
  Alert Condition: daily_avg > 5
  Notification Frequency: Each time alert is evaluated

A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

• A. Network latency due to some cluster nodes being in different regions from the source data
• B. Credential validation errors while pulling data from an external system.
• C. Task queueing resulting from improper thread pool assignment.
• D. Skew caused by more data being assigned to a subset of spark-partitions.
• E. Spill resulting from attached volume storage being too small.

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

• A. Yes; both of the streams can share a single checkpoint directory.
• B. No; Delta Lake manages streaming checkpoints in the transaction log.
• C. No; only one stream can write to a Delta Lake table.
• D. No; each of the streams needs to have its own checkpoint directory.
• E. Yes; Delta Lake supports infinite concurrent writers.

The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

• A. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
• B. No; the change data feed only tracks inserts and updates not deleted records.
• C. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
• D. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
• E. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.