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

Section	Weight	Objectives
Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic
Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules
Data Modelling	6%	- Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models
Monitoring and Alerting	10%	- Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads
Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions
Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Data Governance	7%	- Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards
Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs
Cost & Performance Optimisation	13%	- Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A,D

Explanation:
Databricks Jobs supports defining multi-task workflows that include notebooks, SQL statements, and Python wheel tasks. These can be configured with retry policies, dependency chains, and failure notifications. The correct practice, as stated in the documentation, is to use the Jobs REST API (/jobs/create) or Databricks Asset Bundles to define multi-task jobs, and then trigger them programmatically using /jobs/run-now, CLI, or SDK. This allows the team to maintain full job history, handle retries automatically, and receive alerts via configured email notifications. Using
/jobs/runs/submit creates one-off ad hoc runs without maintaining dependency visibility.
Therefore, options B and C together satisfy the operational, automation, and governance requirements.

NEW QUESTION # 204
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. Workspace audit logs
B. Driver's log file
C. Executor's log file
D. Ganglia
E. Cluster Event Log

Answer: E

NEW QUESTION # 205
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
B. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Answer: C

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

NEW QUESTION # 206
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
C. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
D. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.

Answer: C

Explanation:
In Databricks, owning a catalog or schema does not automatically grant access to the tables within it. Table-level permissions are separate, so even the schema or catalog owner must be explicitly granted privileges on individual tables or use the ability to grant themselves access.

NEW QUESTION # 207
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
B. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
C. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
D. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.

Answer: B

Explanation:
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

NEW QUESTION # 208
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