Question 1

You are asked to setup the alert to notify every time once the data is loaded into a reporting table, team also asked to include the number of records in the alert email notification.

Options :

A : Use notebook and python code to run every minute, using python variables to capture send the information in an email

B : Setup an alert but use the default template to notify the message in email’s subject

C : Setup an alert but use the custom template to notify the message in email’s subject

D : Use the webhook destination instead so alert message can be customized

E : Use custom email hook to customize the message

Answer: C

Question 2

you are currently working on creating a spark stream process to read and write in for a one-time micro batch, and also rewrite the existing target table, fill in the blanks to complete the below command sucesfully.spark.table("source_table").writeStream.option("____", “dbfs:/location/silver").outputMode("____").trigger(Once=____).table("target_table")

Options :

A : checkpointlocation, complete, True

B : targetlocation, overwrite, True

C : checkpointlocation, True, overwrite

D : checkpointlocation, True, complete

E : checkpointlocation, overwrite, True

Answer: A

Question 3

Which of the following commands can be used to read a delta table in SQL

Options :

A : Spark.sql(“select * from table_name”)

B : %sql Select * from table_name

C : Both A & B

D : Execute.sql(“select * from table”)

E : Delta.sql(“select * from table”)

Answer: C

Question 4

At the end of the inventory process a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data incrementally, schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically. Below is the auto loader to command to load the data, fill in the blanks for successful execution of below code.

spark.readStream

.format("cloudfiles")

.option("_______",”csv)

.option("_______", ‘dbfs:/location/checkpoint/’)

.load(data_source)

.writeStream

.option("_______",’ dbfs:/location/checkpoint/’)

.option("_______", "true")

.table(table_name))

Options :

A : format, checkpointlocation, schemalocation, overwrite

B : cloudfiles.format, checkpointlocation, cloudfiles.schemalocation, overwrite

C : cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema

D : cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, append

E : cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, overwrite

Answer: C

Question 5

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.

Which approach can the data engineering team use to improve the latency of the team’s queries?

Options :

A : They can increase the cluster size of the SQL endpoint.

B :

They can increase the maximum bound of the SQL endpoint’s scaling range.

C :

They can turn on the Auto Stop feature for the SQL endpoint.

D :

They can turn on the Serverless feature for the SQL endpoint.

E :

They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”

Answer: B

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