Databricks-Certified-Professional-Data-Engineer Valid Test Pattern | Latest Databricks-Certified-Professional-Data-Engineer Test Report

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

Section	Objectives
Data Modeling and Storage	- Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies
Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance
Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads
Data Ingestion and Transformation	- Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
Which method can be used to determine the total wall-clock time it took to execute a query?

A. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
B. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
C. Open the Query Profiler associated with that query and use the Aggregated task time metric.
D. In the Spark UI, take the job duration of the longest-running job associated with that query.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

NEW QUESTION # 132
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.
B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
D. Records that violate the expectation cause the job to fail.
E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Answer: E

Explanation:
Explanation
The answer is, Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

NEW QUESTION # 133
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors group can view the job but not modify or run it.
* Avoid granting unintended permissions to other users or groups.
How should the data engineer deploy the job while meeting the requirements?

A. permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
B. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW
C. resources:
jobs:
my-job:
name: data-pipeline
tasks: (...)
job_clusters: (...)
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
D. resources:
jobs:
my-job:
name: data-pipeline
tasks: (...)
job_clusters: (...)
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW

Answer: C

Explanation:
Databricks documents that resource-specific permissions for bundle resources can be defined under the resource itself, such as resources.jobs. < job > .permissions , using group_name and level . The documented syntax supports CAN_VIEW , CAN_MANAGE , and related permission levels, which matches option B. ( Databricks Documentation ) Option C is invalid because it repeats the permissions key incorrectly. Option D applies top-level permissions more broadly across bundle resources instead of scoping them specifically to the job, which does not best satisfy the "avoid unintended permissions" requirement. Option B is therefore the correct and properly scoped configuration. ( Databricks Documentation )
======

NEW QUESTION # 134
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team.
The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?

A. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
B. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
C. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
D. Require the analytics team to use a tool that supports Delta table.

Answer: A

Explanation:
Delta Lake introducedDelta Universal Format (Delta UniForm), which allowsseamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can beconverted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
* (A) Require the analytics team to use a tool that supports Delta table
* Incorrect:While Delta Lake is widely used, requiring the team to change toolsis not a flexible or scalable solution.
* (B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table
* Incorrect:
* The uniform featuremust be enabled after conversion.
* You cannotdirectly enable uniformwithout firstconverting the table.
* (C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team
* Incorrect:
* Creating a separate Iceberg copy wouldduplicate storage and increase maintenance complexity.
* This is not necessary whenDelta UniForm allows direct compatibility with Iceberg.
* (D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table
* Correct:
* The best approach is toconvert the existing Delta table to Icebergusing the Databricks Delta to Iceberg migration tools.
* After conversion,enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice forinteroperability between Delta and Icebergis toconvert the Delta table to Iceberg and enable uniform, ensuringcross-compatibilitywithout data duplication.Thus,Option (D) is the correct answer.
References:
* Delta UniForm for Apache Iceberg - Databricks Documentation
* Convert Delta to Iceberg - Databricks

NEW QUESTION # 135
Which of the following technique can be used to implement fine-grained access control to rows and columns of the Delta table based on the user's access?

A. Data access control lists
B. Row and column access control lists
C. Use Unity catalog to grant access to rows and columns
D. Dynamic Access control lists with Unity Catalog
E. Use dynamic view functions

Answer: E

Explanation:
Explanation
The answer is, Use dynamic view functions.
Here is an example that limits access to rows based on the user being part managers group, in the below view if a user is not a part of the manager's group you can only see rows where the total amount is <= 1000000 Dynamic view function to filter rows
1.CREATE VIEW sales_redacted AS
2.SELECT user_id, country, product, total
3.FROM sales_raw
4.WHERE CASE WHEN is_member('managers') THEN TRUE ELSE total <= 1000000 END; Dynamic view function to hide a column data based on user's access,
1.CREATE VIEW sales_redacted AS
2.SELECT user_id,
3. CASE WHEN is_member('auditors') THEN email ELSE 'REDACTED' END AS email,
4. country,
5. product,
6. total
7.FROM sales_raw
Please review below for more details
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#dynamic-v

NEW QUESTION # 136
......

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