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

Databricks Certified Data Engineer Professional Sample Questions:

1. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
B) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
C) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
D) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
E) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.

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

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

3. A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

A) databricks execute my_project_job -e prod
B) databricks run my_project_job -t prod
C) databricks bundle run my_project_job -t prod
D) databricks job run my_project_job --env prod

4. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
D) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

5. The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:

What does the data governance team need to do next to achieve this goal?

A) CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
B) CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
C) CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
D) CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);

Solutions: