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Databricks Certified Data Engineer Professional Sample Questions:

1. 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_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B) @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")
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("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D) @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")

2. 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) COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
C) ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
D) APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');

3. Which statement regarding spark configuration on the Databricks platform is true?

A) Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
B) Spark configuration properties can only be set for an interactive cluster by creating a global init script.
C) Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
D) The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
E) When the same spar configuration property is set for an interactive to the same interactive cluster.

4. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
B) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
C) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
D) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
E) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

5. A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A) A lightweight single-node cluster with low worker node count to reduce costs.
B) A high-concurrency cluster designed for interactive SQL workloads.
C) An all-purpose cluster always kept running to ensure low-latency job startup times.
D) A job cluster configured to autoscale across multiple workers during the pipeline run.

Solutions: