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

Databricks Certified Data Engineer Professional Sample Questions:

1. The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

A) ZORDER ON review will need to be run to see performance gains.
B) Delta Lake statistics are only collected on the first 4 columns in a table.
C) Text data cannot be stored with Delta Lake.
D) The Delta log creates a term matrix for free text fields to support selective filtering.
E) Delta Lake statistics are not optimized for free text fields with high cardinality.

2. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
B) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
C) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
D) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.

3. A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D) @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

4. A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)

A) Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
B) Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
C) Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
D) Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
E) Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.

5. A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A) Both commands will fail. No new variables, tables, or views will be created.
B) Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
C) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
D) Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
E) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

Solutions: