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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a challenging and rewarding certification exam that can help data engineers to validate their skills and knowledge 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. If you are a data engineer who is interested in working with Databricks, then this certification exam is definitely worth considering.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can set up the dashboard's SQL endpoint to be serverless.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can reduce the cluster size of the SQL endpoint.
E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: C

Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard

NEW QUESTION # 91
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. O They can set up the dashboard's SQL endpoint to be serverless.
B. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
C. O They can reduce the cluster size of the SQL endpoint.
D. Q They can turn on the Auto Stop feature for the SQL endpoint.

Answer: D

Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
Reference:
Databricks documentation on SQL endpoints: SQL Endpoints in Databricks

NEW QUESTION # 92
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain valuable data than Silver tables.
B. Gold tables are more likely to contain aggregations than Silver tables.
C. Gold tables are more likely to contain more data than Silver tables.
D. Gold tables are more likely to contain a less refined view of data than Silver tables.
E. Gold tables are more likely to contain truthful data than Silver tables.

Answer: D

NEW QUESTION # 93
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

A. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
B. There is no way to determine why a Job task is running slowly.
C. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
D. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
E. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.

Answer: A

Explanation:
The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. References: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance

NEW QUESTION # 94
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

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

Answer: A

Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation

NEW QUESTION # 95
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