Question 1

Which of the following statements best describes Delta Lake Auto Compaction?

Options :

A : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job with Z-Ordering toward a file size of 128 MB

B : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job without Z-Ordering toward a file size of 128 MB

C : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job with Z-Ordering toward a file size of 1 GB.

D : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job without Z-Ordering toward a file size of 1 GB.

E : None of the above statements correctly describe Delta Lake Auto Compaction.

Answer: B

Question 2

The data engineering team has a Silver table called ‘sales_cleaned’ where new sales data is appended in near real-time.

They want to create a new Gold-layer entity against the ‘sales_cleaned’ table to calculate the year-to-date (YTD) of the sales amount. The new entity will have the following schema:

country_code STRING, category STRING, ytd_total_sales FLOAT, updated TIMESTAMP

It’s enough for these metrics to be recalculated once daily. But since they will be queried very frequently by several business teams, the data engineering team wants to cut down the potential costs and latency associated with materializing the results.

Which of the following solutions meets these requirements?

Options :

A : Define the new entity as a view to avoid persisting the results each time the metrics are recalculated

B : Define the new entity as a global temporary view since it can be shared between notebooks or jobs that share computing resources.

C : Configuring a nightly batch job to recalculate the metrics and store them as a table overwritten with each update

D : Create multiple tables, one per business team so the metrics can be queried quickly and efficiently.

E : All the above solutions meet the required requirements since Databricks uses the Delta Caching feature

Answer: C

Question 3

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.

Which approach will allow this developer to review the current logic for this notebook?

Options :

A : Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

B : Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

C : Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

D : Merge all changes back to the main branch in the remote Git repository and clone the repo again

E : Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository

Answer: B

Question 4

A data engineer wants to upload a CSV file from local system storage to the DBFS of a Databricks workspace. They have Databricks CLI already configured on the local system.

Which of the following Databricks CLI commands can the data engineer use to complete this task ?

Options :

A : workspace

B : fs

C : jobs

D : configure

E : libraries

Answer: B

Question 5

Which of the following commands allows data engineers to perform an insert-only merge?

Options :

A : MERGE INTO orders

USING new_orders

ON orders.orders_id = new_orders.orders_id

WHEN MATCHED

INSERT *

B : MERGE INTO orders

USING new_orders

ON orders.orders_id = new_orders.orders_id

WHEN NOT MATCHED

INSERT *

C : MERGE INTO orders

USING new_orders

ON orders.orders_id = new_orders.orders_id

WHEN MATCHED

INSERT *

WHEN NOT MATCHED

IGNORE *

D : MERGE INTO orders

USING new_orders

ON orders.orders_id = new_orders.orders_id

WHEN NOT MATCHED

INSERT *

WHEN MATCHED

IGNORE *

E : MERGE INTO orders

USING new_orders

ON orders.orders_id = new_orders.orders_id

WHEN NOT MATCHED

INSERT *

ELSE IGNORE *

Answer: B

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