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

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
B) Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
C) Instruct the marketing team to download results as a CSV and email them to the sales organization.
D) 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.
E) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

2. A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A) GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B) GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C) GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D) GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

3. When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

A) The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
B) The group needs to have USE CATALOG and USE SCHEMA on the catalog.
C) The group needs to have ALL PRIVILEGES on the catalog.
D) The group should be made OWNER of the catalog.

4. Which statement regarding spark configuration on the Databricks platform is true?

A) Spark configuration properties can only be set for an interactive cluster by creating a global init script.
B) When the same spar configuration property is set for an interactive to the same interactive cluster.
C) Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
D) Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
E) The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.

5. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Spill resulting from attached volume storage being too small.
B) Skew caused by more data being assigned to a subset of spark-partitions.
C) Network latency due to some cluster nodes being in different regions from the source data
D) Task queueing resulting from improper thread pool assignment.
E) Credential validation errors while pulling data from an external system.

Solutions: