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Databricks Certified Data Engineer Professional Sample Questions:

1. A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A) Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
B) Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
C) Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
D) Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.

2. A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A) Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
B) Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
C) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
D) Convert the list of configuration values to a dictionary of table settings, using table names as keys.

3. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Workspace audit logs
B) Executor's log file
C) Driver's log file
D) Cluster Event Log
E) Ganglia

4. A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A) Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
B) Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
C) Enable auto-compaction (optimizeWrite and autoCompact).
D) Enable Delta caching.

5. A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A) Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
B) Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
C) Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
D) Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
E) Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

Solutions: