FREE PDF QUIZ 2025 PERFECT DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE: DATABRICKS CERTIFIED DATA ENGINEER ASSOCIATE EXAM PRACTICE EXAMS

Free PDF Quiz 2025 Perfect Databricks-Certified-Data-Engineer-Associate: Databricks Certified Data Engineer Associate Exam Practice Exams

Free PDF Quiz 2025 Perfect Databricks-Certified-Data-Engineer-Associate: Databricks Certified Data Engineer Associate Exam Practice Exams

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

  • A. Pull
  • B. Merge
  • C. Clone
  • D. Push
  • E. Commit

Answer: A

Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). References:
* Run Git operations on Databricks Repos
* Git pull


NEW QUESTION # 75
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

  • A. spark.sql
  • B. There is no way to share data between PySpark and SQL.
  • C. SELECT * FROM sales
  • D. spark.delta.table
  • E. spark.table

Answer: A

Explanation:
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). References: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.


NEW QUESTION # 76
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

  • A. DELTA
  • B. org.apache.spark.sql.sqlite
  • C. autoloader
  • D. org.apache.spark.sql.jdbc
  • E. sqlite

Answer: B


NEW QUESTION # 77
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 Development 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 shut down. The compute resources will persist to allow for additional testing.
  • D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
  • E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: B

Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution


NEW QUESTION # 78
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 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.
  • 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 once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: D


NEW QUESTION # 79
......

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