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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

A) Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
B) Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
C) Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
D) Call/api/2.1/jobs/runs/get with the run_id and include_history parameters

2. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) delayWrite("event_time", "10 minutes")
B) awaitArrival("event_time", "10 minutes")
C) withWatermark("event_time", "10 minutes")
D) await("event_time + `10 minutes'")
E) slidingWindow("event_time", "10 minutes")

3. The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A) Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
C) Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D) Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
E) Define a view against the products_per_order table and define the dashboard against this view.

4. 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_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")
D) @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")

5. A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A) SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
B) APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
C) COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
D) ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');

Solutions: