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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A) Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
B) Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
C) For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
D) Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.

2. A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A) Move files to a volume and read with SQL editor.
B) Use Auto Loader and set cloudFiles.format to "IMAGE".
C) Use Auto Loader and set cloudFiles.format to "BINARYFILE".
D) Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
E) Use Auto Loader and set cloudFiles.format to "TEXT".

3. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
B) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
C) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
D) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
E) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

4. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
B) A user can only transfer job ownership to a group if they are also a member of that group.
C) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D) Other than the default "admins" group, only individual users can be granted privileges on jobs.
E) Only workspace administrators can grant "Owner" privileges to a group.

5. Review the following error traceback:

Which statement describes the error being raised?

A) There is no column in the table named heartrateheartrateheartrate
B) The code executed was PvSoark but was executed in a Scala notebook.
C) There is a type error because a column object cannot be multiplied.
D) There is a syntax error because the heartrate column is not correctly identified as a column.
E) There is a type error because a DataFrame object cannot be multiplied.

Solutions: