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

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Cluster Event Log
B) Driver's log file
C) Executor's log file
D) Workspace audit logs
E) Ganglia

2. The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A) Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
B) Group members have full permissions on the prod database and can also assign permissions to other users or groups.
C) Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
D) Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
E) Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

3. A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A) df.withColumn ("user_email", F.sha2 ("user_email"))
B) df.withColumn ("hashed_email", sha2 ("user_email"))
C) df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
D) df.withColumn ("user_emai", F.expr("uuid()"))

4. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
B) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
C) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
D) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.

5. A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)

A) Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
B) Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
C) Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
D) Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
E) Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.

Solutions: