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Databricks Certified Professional Data Engineer Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Run source env/bin/activate in a notebook setup script
C. Use &Pip install in a notebook cell
D. Install libraries from PyPi using the cluster UI

Answer: D

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
Reference:
Databricks Documentation on Libraries: Libraries

NEW QUESTION # 32
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_or_drop( " 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( " 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 " )

Answer: D

Explanation:
Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect, @dlt.
expect_or_drop, and @dlt.expect_all.
* @dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows . Instead, failing rows can be written to a quarantine table.
* @dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.
In this case, the requirement is:
* Good rows (reading < 120) go to the silver table .
* Bad rows (reading > = 120) go to a quarantine table .
* Bad rows should not be included in silver.
The correct implementation is Option A , where:
* The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.
* The quarantine table applies an expectation for reading > = 120, ensuring bad records are captured separately.
Other options are incorrect:
* Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.
* Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.
Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.
Reference: Databricks Documentation - Delta Live Tables (DLT) Expectations: @dlt.expect, @dlt.
expect_or_drop, and Quarantine Tables.

NEW QUESTION # 33
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB.
How should the data engineer meet this requirement?

A. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
B. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
C. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
D. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks documentation describes Liquid Clustering as the recommended data layout optimization for evolving workloads. Unlike traditional partitioning or Z-ordering, Liquid Clustering dynamically maintains data organization without rewriting existing files when clustering keys change. It handles data skew automatically and supports flexible re-clustering based on query patterns. Partitioning and Z-ordering require full data rewrites whenever key structures change, making them expensive for tables with frequently evolving access patterns. For tables under a few terabytes, Liquid Clustering offers the best balance between scalability, adaptability, and maintenance efficiency. Thus, option C aligns with Databricks' best practice for modern adaptive layout optimization.

NEW QUESTION # 34
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company.
A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users.
Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B. ''Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
D. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.

Answer: D

Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.
Reference:
Databricks Documentation on Secret Management: Secrets

NEW QUESTION # 35
Which of the following is true of Delta Lake and the Lakehouse?

A. Z-order can only be applied to numeric values stored in Delta Lake tables
B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Answer: D

Explanation:
Explanation
This is the correct answer because it is true of Delta Lake and the Lakehouse. Delta Lake uses Parquet as the underlying storage format for data files. Parquet is a columnar format that compresses data by column rather than by row. This means that Parquet can achieve high compression ratios for columns that have low cardinality or high repetition of values, such as integers, booleans, or dates. However, for columns that have high cardinality or low repetition of values, such as strings, Parquet cannot compress data very well.
Therefore, strings will only be compressed when a character is repeated multiple times within a row. Verified References:[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features - Schema enforcement and evolution" section.

NEW QUESTION # 36
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