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Exam Code: Databricks-Certified-Data-Analyst-Associate Vendor: Databricks Name: Databricks Certified Data Analyst Associate Exam 118 Q&A with Explanation Last Update: Jul 14, 2026
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Databricks Certified Data Analyst Associate Exam Questions and Answers

Questions 1

A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.

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?

Options:

A.

They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints.

B.

They can set up the dashboard’s SQL endpoint to be serverless.

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 is not one of the included query’s SQL endpoint.

Questions 2

The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.

After logging back in two days later, what is the status of the stakeholders.eur_customers view?

Options:

A.

The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.

B.

The view has been dropped.

C.

The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.

D.

The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.

E.

The view has been converted into a table.

Questions 3

Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Options:

A.

( spark.readStream.load(rawSalesLocation) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

B.

( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

C.

( spark.table( " sales " ) .withColumn( " avgPrice " , col( " sales " ) / col( " units " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

D.

( spark.table( " sales " ) .filter(col( " units " ) > 0) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " append " ) .table( " newSales " ))

E.

( spark.table( " sales " ) .groupBy( " store " ) .agg(sum( " sales " )) .writeStream .option( " checkpointLocation " , checkpointPath) .outputMode( " complete " ) .table( " newSales " ))

F.

Option A

G.

Option B

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