Question 1

A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

Options :

A : ALL PRIVILEGES on the finance_data catalog.

B : Make the finance team lead a metastore admin.

C : GRANT OPTION privilege on the finance_data catalog.

D : MANAGE privilege on the finance_data catalog.

Answer: D

Question 2

Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets. Which statement describes a limitation of Databricks Secrets?

Options :

A : Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.

B : Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.

C : Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.

D : Iterating through a stored secret and printing each character will display secret contents in plain text.

E : The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.

Answer: D

Question 3

The data engineering team maintains the following code:

Databricks-Certified-Professional-Data-Engineer-page61-image9

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

Options :

A :

A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

B :

The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

C :

An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.

D :

An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table

E :

No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

Answer: B

Question 4

A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

Options :

A : Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

B : The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.

C : Shuffle Hash Joins are always more efficient than Sort Merge Joins.

D : The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Answer: A

Question 5

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

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