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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.

2. A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A) They are ignored.
B) They are merged.
C) They are updated.
D) They are deleted.
E) They are inserted.

3. A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

A) hash(email)
B) sha1(email)
C) mask(email, '?')
D) sha2(email, 0)

4. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
B) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
C) Instruct the marketing team to download results as a CSV and email them to the sales organization.
D) Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
E) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

5. A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A) Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
B) Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
C) Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
D) Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

Solutions: