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Databricks Certified-Data-Engineer-Professional Exam Overview:

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Question 2

A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
B. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
C. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
D. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
E. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.

Question 3

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
B. The source query failed to update properly for three consecutive minutes and then restarted
C. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

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

Question 5

Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
B. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
C. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
D. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.

Solutions: