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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
B. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
C. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
D. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

Answer: B

Explanation:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A -- automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 109
A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Use a SQL CONSTRAINT block referencing the JSON file path.
B. Invoke an external API to validate records against the metadata rules.
C. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
D. Reference each expectation with @dlt.expect decorators in the table declaration.

Answer: C

Explanation:
Lakeflow Declarative Pipelines provide the expect_all method for programmatically applying multiple data quality expectations at once. The documentation explains that @dlt.expect_all accepts a dictionary of expectation names mapped to SQL constraints, allowing rules to be dynamically loaded from metadata such as JSON files. This ensures that pipelines remain maintainable and scalable without needing to hardcode individual @dlt.expect decorators. The event logs will track each expectation's pass and fail counts individually, making it auditable.

NEW QUESTION # 110
What statement is true regarding the retention of job run history?

A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
B. It is retained for 60 days, after which logs are archived
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, during which you can export notebook run results to HTML
E. It is retained until you export or delete job run logs

Answer: D

NEW QUESTION # 111
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. jobs
B. configure
C. workspace
D. libraries
E. fs

Answer: E

Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

NEW QUESTION # 112
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 4
100 GB per Executor
40 Cores/Executor
B. Total VMs: 1
400 GB per Executor
160 Cores / Executor
C. Total VMs: 8
50 GB per Executor
20 Cores / Executor
D. Total VMs: 2
200 GB per Executor
80 Cores / Executor

Answer: B

Explanation:
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 113
......

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