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Databricks Certified Data Engineer Professional Sample Questions:

1. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
B) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
C) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
D) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

2. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
B) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
C) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
D) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
E) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

3. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
E) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

4. 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 Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
B) 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.
C) 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.
D) Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.

5. Which of the following is true of Delta Lake and the Lakehouse?

A) Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
B) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
C) Z-order can only be applied to numeric values stored in Delta Lake tables
D) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Solutions: