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

Section	Weight	Objectives
Topic 1: Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads
Topic 2: Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines
Topic 3: Data Governance	7%	- Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata
Topic 4: Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health
Topic 5: Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code
Topic 6: Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions
Topic 7: Data Transformation, Cleansing, and Quality	10%	- Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management
Topic 8: Data Sharing and Federation	5%	- Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation
Topic 9: Cost & Performance Optimisation	13%	- Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources
Topic 10: Data Modelling	6%	- Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

A. SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B. SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
C. SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D. SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

Answer: B

Explanation:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non- existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

NEW QUESTION # 141
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
B. Stop the existing pipeline; use the returned settings in a reset command
C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
D. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git

Answer: C

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

NEW QUESTION # 142
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the SkipChangeCommits flag to true raw_lot
B. Set the pipelines, reset, allowed property to false on bpm_stats
C. Set the pipelines, reset, allowed property to false on raw_iot
D. Set the skipChangeCommits flag to true on bpm_stats

Answer: C

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.

NEW QUESTION # 143
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 a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
C. 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.
D. 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.

Answer: D

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 # 144
Which statement describes the default execution mode for Databricks Auto Loader?

A. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
B. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
C. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
E. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.

Answer: C

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.

NEW QUESTION # 145
......

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