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Databricks Certified Data Engineer Professional Sample Questions:

1. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Executor's log file
B) Ganglia
C) Cluster Event Log
D) Workspace audit logs
E) Driver's log file

2. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Use a SQL CONSTRAINT block referencing the JSON file path.
B) Invoke an external API to validate records against the metadata rules.
C) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
D) Reference each expectation with @dlt.expect decorators in the table declaration.

3. 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) 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.
B) All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
C) Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
D) Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.

4. A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A) The table reference in the metastore is updated and all data files are moved.
B) All related files and metadata are dropped and recreated in a single ACID transaction.
C) The table name change is recorded in the Delta transaction log.
D) A new Delta transaction log Is created for the renamed table.
E) The table reference in the metastore is updated and no data is changed.

5. A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A) The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
B) The source data is valid JSON but does not conform to the defined schema in some way.
C) At some point, the upstream data provider switched everything to multi-line JSON.
D) The badRecordsPath location is accumulating many small files.

Solutions: