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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q191-Q196):

NEW QUESTION # 191
A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
B. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
C. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
E. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

Answer: A

Explanation:
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed.
This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.

NEW QUESTION # 192
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
D. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"

Answer: D

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 193
Which statement regarding spark configuration on the Databricks platform is true?

A. When the same spar configuration property is set for an interactive to the same interactive cluster.
B. Spark configuration set within an notebook will affect all SparkSession attached 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 properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
E. Spark configuration properties can only be set for an interactive cluster by creating a global init script.

Answer: D

Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.

NEW QUESTION # 194
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
B. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
C. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
D. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.

Answer: C

Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.

NEW QUESTION # 195
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Read
B. Can run
C. Can manage
D. Can edit

Answer: A

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.

NEW QUESTION # 196
......

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