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

Section	Weight	Objectives
Topic 1: Monitoring and Alerting	10%	- Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring
Topic 2: Data Governance	7%	- Data lineage and metadata tracking - Policy enforcement - Unity Catalog management
Topic 3: Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Topic 4: Ensuring Data Security and Compliance	10%	- Data encryption and masking - Access control and permissions - Compliance standards implementation
Topic 5: Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks
Topic 6: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices
Topic 7: Data Modelling	6%	- Delta Lake table design - Schema design and management - Medallion Architecture implementation
Topic 8: Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic
Topic 9: Cost & Performance Optimisation	13%	- Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing)
Topic 10: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q157-Q162):

NEW QUESTION # 157
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 invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints 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 exists; CHECK constraints can only be added during initial table creation.
B. 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.
C. 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.
D. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
E. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

Answer: C

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 158
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
C. The connection to the external table will succeed; the string "redacted" will be printed.
D. The connection to the external table will fail; the string "redacted" will be printed.
E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Answer: C

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.

NEW QUESTION # 159
You noticed that colleague is manually copying the notebook with _bkp to store the previous ver-sions, which of the following feature would you recommend instead.

A. Databricks notebook can be exported as HTML and imported at a later time
B. Databricks notebooks can be exported into dbc archive files and stored in data lake
C. Databricks notebooks should be copied to a local machine and setup source control locally to version the notebooks
D. Databricks notebooks support change tracking and versioning

Answer: D

Explanation:
Explanation
Answer is Databricks notebooks support automatic change tracking and versioning.
When you are editing the notebook on the right side check version history to view all the changes, every change you are making is captured and saved.

NEW QUESTION # 160
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. return spark.read.option( " readChangeFeed " , " true " ).table ( " bronze " )
C.
D. return spark.readStream.table( " bronze " )
E. return spark.readStream.load( " bronze " )

Answer: A

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 161
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
B. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
C. The update throws an error because changes to existing columns in the target schema are not supported.
D. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

Answer: B

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.
html#schema-evolution

NEW QUESTION # 162
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