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Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by ema

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview :

Exam Name: Databricks Certified Data Engineer Associate Exam
Exam Code: Databricks-Certified-Data-Engineer-Associate Dumps
Vendor: Databricks Certification: Databricks Certification
Questions: 159 Q&A's Shared By: ema
Question 40

An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.

Which of the following approaches can the manager use to ensure the results of the query are updated each day?

Options:

A.

They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.

B.

They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.

C.

They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.

D.

They can schedule the query to run every 1 day from the Jobs UI.

E.

They can schedule the query to run every 12 hours from the Jobs UI.

Discussion
Question 41

A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.

Which of the following approaches could be used by the data engineering team to complete this task?

Options:

A.

They could submit a feature request with Databricks to add this functionality.

B.

They could wrap the queries using PySpark and use Python’s control flow system to determine when to run the final query.

C.

They could only run the entire program on Sundays.

D.

They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.

E.

They could redesign the data model to separate the data used in the final query into a new table.

Discussion
Question 42

A global retail company sells products across multiple categories (e.g.. Electronics, Clothing) and regions (e.g.. North. South, East. West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

Questions 42

Options:

A.

Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))

B.

Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))

C.

Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))

D.

Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))

Discussion
Amy
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Question 43

A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.

Which of the following explains why the data files are no longer present?

Options:

A.

The VACUUM command was run on the table

B.

The TIME TRAVEL command was run on the table

C.

The DELETE HISTORY command was run on the table

D.

The OPTIMIZE command was nun on the table

E.

The HISTORY command was run on the table

Discussion
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