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The Databricks Databricks-Certified-Data-Engineer-Associate Exam is intended for data engineers, data architects, and developers who are responsible for designing, building, and maintaining data pipelines. Databricks Certified Data Engineer Associate Exam certification exam is comprised of 60 multiple-choice questions, and candidates have 90 minutes to complete the exam. Databricks-Certified-Data-Engineer-Associate exam measures candidates' knowledge and skills in various areas, including data ingestion, data transformation, and data processing.

Databricks Certified Data Engineer Associate certification is a valuable credential for data engineers who want to demonstrate their expertise in working with Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized by leading organizations and can help data engineers to advance their careers. Databricks Certified Data Engineer Associate Exam certification is also a great way for data engineers to demonstrate their commitment to ongoing professional development and to stay up to date with the latest trends and technologies in the field.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q186-Q191):

NEW QUESTION # 186
A data engineer maintains a Declarative Automation Bundle (formerly Databricks Asset Bundle), which deploys a dashboard.

Which set of actions will enable the dashboard to be deployed in prod using catalog_prod without changing the resource definition?

A. Run databricks bundle deploy --target prod so the deployment uses targets.prod and overrides dataset_catalog to catalog_prod.
B. Run databricks bundle deploy --var dataset_catalog=catalog_prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.
C. Run databricks bundle deploy --profile prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.
D. Run databricks bundle execute --profile prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.

Answer: A

Explanation:
databricks bundle deploy --target prod selects the prod target, which overrides dataset_catalog with catalog_prod. A CLI profile controls authentication, not bundle target selection.

NEW QUESTION # 187
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

The data engineer only wants the query to process all of the available data in as many batches as required.
Which line of code should the data engineer use to fill in the blank?

A. trigger(once=True)
B. trigger(continuous="once")
C. trigger(processingTime="once")
D. trigger(availableNow=True)

Answer: D

NEW QUESTION # 188
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE 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 dropped from the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
C. Records that violate the expectation cause the job to fail.
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Answer: C

Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
References:
* Delta Live Tables Expectations
* [Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 189
A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?

A. select
B. groupBy
C. orderBy
D. filter

Answer: B

NEW QUESTION # 190
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT ON TABLE sales TO team;
B. GRANT USAGE ON TABLE sales TO team;
C. GRANT ALL PRIVILEGES ON TABLE team TO sales;
D. GRANT ALL PRIVILEGES ON TABLE sales TO team;
E. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;

Answer: D

Explanation:
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. Reference: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks

NEW QUESTION # 191
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