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Databricks Certified Data Engineer Professional Sample Questions:

1. 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 CAN RUN permission to everyone and CAN MANAGE to a single admin group.
C) Grant all users CAN MANAGE permission on all jobs to avoid access issues.
D) Use only folder-level permissions and avoid setting permissions on individual jobs.

2. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
B) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C) Configure a job that executes every time new data lands in a given directory.
D) Schedule a job to execute the pipeline once an hour on a new job cluster.

3. The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
B) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
C) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
E) No; the change data feed only tracks inserts and updates not deleted records.

4. Which statement characterizes the general programming model used by Spark Structured Streaming?

A) Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
B) Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
C) Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D) Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E) Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

5. A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

A) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
.expect_or_drop("valid_amount", "amount > 0")
)
B) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect("valid_customer", "customer_id IS NOT NULL")
.expect("valid_amount", "amount > 0")
)
C) @dlt.table
@dlt.expect("valid_customer", "customer_id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
D) @dlt.table
@dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")