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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
C. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
D. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
E. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Question 2

Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The filter is executed only after the full data scan, preventing data skipping.
B. The Delta table lacks optimization that enables dynamic file pruning.
C. The filter condition involves a data type excluded from data skipping support.
D. The event_date column is outside the table's partitioning and Z-ordering scheme.

Question 3

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
B. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
D. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
E. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

Question 4

A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
B. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
C. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
D. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

Question 5

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A.

B.

C. return spark.read.option("readChangeFeed", "true").table ("bronze")
D. return spark.readStream.load("bronze")
E. return spark.readStream.table("bronze")

Solutions: