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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A) Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
B) Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
C) Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
D) Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.

2. A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A) Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B) Grant all users CAN MANAGE permission on all jobs to avoid access issues.
C) Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
D) Use only folder-level permissions and avoid setting permissions on individual jobs.

3. A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A) Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
B) The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
C) Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
D) Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
E) Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

4. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
C) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
E) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

5. A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

A) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
B) permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
C) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER
D) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW

Solutions: