Increase Chances Of Success With Databricks Databricks-Certified-Data-Engineer-Professional Exam Dumps​

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

Answer: A

Explanation:
Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations. Expectations can either:
Track violations (expect) -> records that do not meet conditions are flagged but still included in the pipeline.
Drop violations (expect_or_drop) -> records that do not meet conditions are excluded from downstream tables.
Fail pipeline on violations (expect_or_fail) -> records that fail conditions stop the pipeline.
In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount < 0) must be dropped. According to the official documentation, the correct method is .expect_or_drop("expectation_name", "SQL_predicate") applied on the streaming input.
Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.
Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.
Option C uses .expect, which also only flags rows, not drop them.
Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.
Therefore, the correct solution is Option A, which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.


NEW QUESTION # 17
A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

Answer: C

Explanation:
In Unity Catalog, privileges are non-cascading--meaning that granting a privilege (like SELECT) on a catalog does not automatically grant the same privilege on contained objects (schemas, tables, or views). Each object type has its own independent access control hierarchy.
According to the Databricks access control documentation: "Privileges do not automatically cascade from catalog to schema or table levels." Administrators must explicitly grant privileges on each level if users need access across objects. This design ensures tighter governance and least-privilege enforcement. Therefore, option B correctly describes Unity Catalog's privilege model, while A and D incorrectly imply automatic inheritance.


NEW QUESTION # 18
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?

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.


NEW QUESTION # 19
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

Answer: B

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.


NEW QUESTION # 20
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

Answer: A

Explanation:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.


NEW QUESTION # 21
......

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