You are working on a dashboard that takes a long time to load in the browser, due to the fact that each visualization contains a lot of data to populate, which of the following approaches can be taken to address this issue?

• A.Increase size of the SQL endpoint cluster
• B.Increase the scale of maximum range of SQL endpoint cluster
• C.Use Databricks SQL Query filter to limit the amount of data in each visualization
• D.Remove data from Delta Lake
• E.Use Delta cache to store the intermediate results

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A newly joined team member John Smith in the Marketing team currently has access read access to sales tables but does not have access to update the table, which of the following commands help you accomplish this?

• A.GRANT UPDATE ON TABLE table_name TO [email protected]
• B.GRANT USAGE ON TABLE table_name TO [email protected]
• C.GRANT MODIFY ON TABLE table_name TO [email protected]
• D.GRANT UPDATE TO TABLE table_name ON [email protected]
• E.GRANT MODIFY TO TABLE table_name ON [email protected]

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A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?

• A.Date
• B.Post_id
• C.User_id
• D.Post_time

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A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

• A.Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
• B.Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
• C.Records that violate the expectation cause the job to fail.
• D.Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.
• E.Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Correct Answer: B

Explanation
The answer is Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

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A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

• A.Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• B.Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
• C.Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
• D.Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• E.Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

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