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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

A) Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
B) Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
C) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
D) Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.

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

3. 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) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
C) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D) Z-order can only be applied to numeric values stored in Delta Lake tables
E) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

4. A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A) Create a view without selecting the sensitive column.
B) Create a UDF that hardcodes allowed groups and apply it as a column mask.
C) Apply a column mask that references the group_access mapping table in its UDF.
D) Use a row filter to restrict access based on the user's group.

5. A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A) Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
B) Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
C) Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
D) Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.

Solutions: