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

Section	Weight	Objectives
Topic 1: Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools
Topic 2: Developing Code for Data Processing using Python and SQL	~22%	- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures
Topic 3: Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Topic 4: CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Topic 5: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 6: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 7: Monitoring, Logging, and Troubleshooting	~8%	- Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures
Topic 8: Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Topic 9: Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs

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Databricks Certified Data Engineer Professional Sample Questions (Q195-Q200):

NEW QUESTION # 195
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
B. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
C. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
D. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Answer: D

Explanation:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.

NEW QUESTION # 196
A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A. It runs VACUUM on the target table to purge conflicting records.
B. It manually sorts incoming events by timestamp before applying changes.
C. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
D. It ignores deletions if they arrive after updates for the same key.

Answer: C

Explanation:
AUTO CDC uses the sequence_by column to deterministically order change events for each key.
Delete operations create tombstones that are retained until all earlier sequence values have been processed, ensuring that out-of-order delete events are still applied correctly and consistently in the target table.

NEW QUESTION # 197
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The update throws an error because changes to existing columns in the target schema are not supported.
C. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
D. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.

Answer: D

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

NEW QUESTION # 198
A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. streamingDF.join(userLookup, ["user_id"], how="outer")
B. userLookup.join(streamingDF, ["user_id"], how="right")
C. streamingDF.join(userLookup, ["userid"], how="inner")
D. streamingDF.join(userLookup, ["user_id"], how="left")
E. userLookup.join(streamingDF, ["userid"], how="inner")

Answer: A

Explanation:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support-matrix-for-joins-in-streaming-queries

NEW QUESTION # 199
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Shallow clone the table for the analytics team.
B. Deep clone the table for the analytics team.
C. Give the analytics team direct access to the production table.
D. Create a new table for the analytics team using a CTAS statement.

Answer: A

Explanation:
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION # 200
......

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