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Databricks Certified Professional Data Engineer exam is a valuable certification for data professionals who work with the Databricks platform. By demonstrating their expertise in using Databricks for data engineering tasks, candidates can enhance their career prospects and contribute to the success of their organizations.

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Databricks Certified Professional Data Engineer exam consists of a set of performance-based tasks that test the candidate's ability to apply their knowledge and skills to real-world scenarios. Databricks-Certified-Professional-Data-Engineer exam is conducted online and can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer Exam is timed and candidates have to complete the tasks within the given time frame. Databricks-Certified-Professional-Data-Engineer exam is designed in such a way that it assesses the candidate's ability to work with Databricks Unified Analytics Platform and solve complex data engineering problems.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q122-Q127):

NEW QUESTION # 122
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. The connection to the external table will fail; the string "redacted" will be printed.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. 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.
E. The connection to the external table will succeed; the string "redacted" will be printed.

Answer: E

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 # 123
You are currently working to ingest millions of files that get uploaded to the cloud object storage for consumption, and you are asked to build a process to ingest this data, the schema of the file is expected to change over time, and the ingestion process should be able to handle these changes automatically. Which of the following method can be used to ingest the data incrementally?

A. AUTO LOADER
B. Checkpoint
C. COPY INTO
D. Structured Streaming
E. AUTO APPEND

Answer: A

Explanation:
Explanation
The answer is AUTO LOADER,
Use Auto Loader instead of the COPY INTO SQL command when:
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*COPY INTO only directory listing but AUTO LOADER supports File notification method where the Auto Loader continues to ingest files as they arrive in cloud object storage lever-aging cloud provider(Queues and triggers) and Spark's structured streaming.
*Your data schema evolves frequently. Auto Loader provides better support for schema in-ference and evolution. See Configuring schema inference and evolution in Auto Loader.

NEW QUESTION # 124
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. With Open Sharing, recipients cannot access Volumes, Models, or notebooks - only static Delta tables are supported.
B. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
C. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
D. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

Answer: A

Explanation:
According to Databricks' documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables -it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks . Only Databricks-to-Databricks sharing supports dynamic data sharing with update visibility and streaming reads. Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.

NEW QUESTION # 125
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: D

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 126
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights.
Which privilege should be granted to the finance team lead?

A. GRANT OPTION privilege on the finance_data catalog.
B. ALL PRIVILEGES on the finance_data catalog.
C. Make the finance team lead a metastore admin.
D. MANAGE privilege on the finance_data catalog.

Answer: D

Explanation:
The MANAGE privilege in Unity Catalog provides the ability to grant and revoke privileges on the specified object (in this case, a catalog) without giving full administrative access or ownership.
This is the Databricks-recommended approach for delegating governance responsibilities while preserving the principle of least privilege .
By contrast, the ALL PRIVILEGES option grants excessive access (including read and write permissions), and metastore admin status provides global control over all catalogs-far exceeding the requirement. The MANAGE privilege enables the finance team lead to control access to objects within finance_data responsibly while limiting overall administrative exposure.

NEW QUESTION # 127
......

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