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

Databricks Certified Data Engineer Professional Sample Questions:

1. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
B) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
C) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D) Parse the Delta Lake transaction log to identify all newly written data files.

2. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
B) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
C) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
D) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.

3. To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

A) SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B) SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C) SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
D) SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

4. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
D) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
E) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

5. A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

A) CAN MANAGE
B) CAN VIEW
C) CAN ATTACH TO
D) CAN RESTART

Solutions: