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

Databricks Certified Data Engineer Professional Sample Questions:

1. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
D) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
E) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

2. A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B) Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
C) Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
D) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
E) Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

3. A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A) databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
B) databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
C) databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
D) databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)

4. A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A) It manually sorts incoming events by timestamp before applying changes.
B) It ignores deletions if they arrive after updates for the same key.
C) It runs VACUUM on the target table to purge conflicting records.
D) It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.

5. A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A) Use a row filter to restrict access based on the user's group.
B) Create a UDF that hardcodes allowed groups and apply it as a column mask.
C) Create a view without selecting the sensitive column.
D) Apply a column mask that references the group_access mapping table in its UDF.

Solutions: