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The Databricks Certified Data Engineer Associate Exam certification exam is designed to test the candidate's knowledge of Databricks and their ability to build and manage data pipelines using the platform. Databricks-Certified-Data-Engineer-Associate exam consists of multiple-choice questions and is timed, with a maximum time limit of 120 minutes. To pass the exam, candidates must achieve a minimum score of 70%. Databricks-Certified-Data-Engineer-Associate Exam is available online and can be taken from anywhere in the world, making it accessible to data engineers from all backgrounds and locations.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification for professionals who want to validate their skills and knowledge in data engineering. Databricks-Certified-Data-Engineer-Associate exam is designed to test the candidate's ability to work with data engineering tools and technologies, including Apache Spark, Apache Hadoop, and Databricks.

Databricks Certified Data Engineer Associate certification exam is ideal for data engineers who work with Databricks on a regular basis and want to enhance their skills and knowledge. Databricks-Certified-Data-Engineer-Associate Exam is also suitable for data engineers who are looking to validate their expertise in Databricks and demonstrate their ability to work with the platform. By earning this certification, data engineers can showcase their proficiency in Databricks and increase their career opportunities in the field of data engineering.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail. Which feature of Delta Live Tables meets this requirement?

A. Expectations
B. Monitoring
C. Error Handling
D. Change Data Capture

Answer: A

Explanation:
Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.

NEW QUESTION # 124
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger("5 seconds")
B. trigger(processingTime="5 seconds")
C. trigger(continuous="5 seconds")
D. trigger()
E. trigger(once="5 seconds")

Answer: B

NEW QUESTION # 125
A data engineer uses COPY INTO to load CSV files from an external location into a Delta table.
The job is accidentally triggered twice within the same hour.
What is the expected outcome?

A. The second run fails with a duplicate-file error and rolls back the first run.
B. The second run loads only files that were not already ingested, because COPY INTO tracks loaded files idempotently.
C. The target table is overwritten with only the newest file.
D. Records are duplicated because COPY INTO always reloads all files in the source path.

Answer: B

Explanation:
COPY INTO maintains state about which source files have already been ingested, so re-running it is idempotent and previously loaded files are skipped. Files are only reprocessed if FORCE = true (or copy_options ('force' = 'true')) is specified.

NEW QUESTION # 126
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
B. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
C. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
D. They can set the query's refresh schedule to end on a certain date in the query scheduler.
E. They can set the query's refresh schedule to end after a certain number of refreshes.

Answer: D

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

NEW QUESTION # 127
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

Answer: E

Explanation:
Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.

NEW QUESTION # 128
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