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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A) Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
B) Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
C) Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
D) Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin

2. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
B) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
C) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
D) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

3. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A) The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
B) Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
C) Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D) Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
E) Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

4. The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A) df.map(lambda x:model(x[columns])).select("customer_id, predictions")
B) df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
C) df.select("customer_id", model(*columns).alias("predictions"))
D) model.predict(df, columns)
E) df.apply(model, columns).select("customer_id, predictions")

5. 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.

Solutions: