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Databricks Certified Data Engineer Professional Sample Questions:

1. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A) DESCRIBE EXTENDED dev.pii test
B) SHOW TABLES dev
C) DESCRIBE HISTORY dev.pii test
D) SHOW TBLPROPERTIES dev.pii test
E) DESCRIBE DETAIL dev.pii test

2. A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A) The Hive metastore is scanned for min and max statistics for the latitude column
B) The Parquet file footers are scanned for min and max statistics for the latitude column
C) The Delta log is scanned for min and max statistics for the latitude column
D) All records are cached to attached storage and then the filter is applied
E) All records are cached to an operational database and then the filter is applied

3. A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
B) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
D) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only

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

5. A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

A)

B)

C)

D)

Solutions: