Prepare with Databricks : Certified-Data-Engineer-Professional exam braindumps as your best preparation materials

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. 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
• C. 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
• 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 engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

• A. It manually sorts incoming events by timestamp before applying changes.
• B. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
• C. It runs VACUUM on the target table to purge conflicting records.
• D. It ignores deletions if they arrive after updates for the same key.

A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

• A. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
• B. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
• C. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
• D. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.

The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

• A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
• B. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
• C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
• D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.