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

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Databricks Certified Data Engineer Professional Sample Questions:

1. The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When a database is being created, make sure that the LOCATION keyword is used.
B) When the workspace is being configured, make sure that external cloud object storage has been mounted.
C) When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
D) When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
E) When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

2. A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A) Store only the date value and calculate all time attributes in queries.
B) Store the date as string in ISO format (YYYY-MM-DD) for readability.
C) Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
D) Create separate dimension tables for different calendar systems (fiscal, academic, etc.)

3. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
B) Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
C) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D) Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
E) Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

4. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
B) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
C) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
D) Share tables WITHOUT HISTORY and enable partitioning for better query performance.

5. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
B) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
D) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
E) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Solutions: