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

An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

• A. A managed table will be created in the DBFS root storage container.
• B. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
• C. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
• D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
• E. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

• A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
• B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
• C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
• D. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

• A. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
• B. Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
• C. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
• D. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.

A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

• A. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
• B. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.
• C. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
• D. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.

A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

• A. Users need the MANAGE SHARES permission on the workspace
• B. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
• C. Any user with USE_CATALOG privilege can create shares
• D. Only workspace admins can create and manage shares