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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A) Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B) Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
C) Grant all users CAN MANAGE permission on all jobs to avoid access issues.
D) Use only folder-level permissions and avoid setting permissions on individual jobs.

2. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
B) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
C) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
D) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
E) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

3. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A) Total VMs: 2
200 GB per Executor
80 Cores / Executor
B) Total VMs: 8
50 GB per Executor
20 Cores / Executor
C) Total VMs: 4
100 GB per Executor
40 Cores/Executor
D) Total VMs: 1
400 GB per Executor
160 Cores / Executor

4. A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A) Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
B) Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
C) Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
D) Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

5. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A) preds.write.format("delta").save("/preds/churn_preds")
B)

C)

D) preds.write.mode("append").saveAsTable("churn_preds")
E)

Solutions: