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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Lakehouse Architecture - Unity Catalog - Delta Lake - Data Management
Topic 2: Data Processing	28%	- Structured Streaming - ETL Pipelines - Spark SQL - Data Transformation
Topic 3: Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality
Topic 4: Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Topic 5: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
B. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
C. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
D. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: B

Explanation:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog.
Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

NEW QUESTION # 205
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_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 skipChangeCommits flag to true on bpm_stats
C. Set the pipelines, reset, allowed property to false on raw_iot
D. Set the pipelines, reset, allowed property to false 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 # 206
Which distribution does Databricks support for installing custom Python code packages?

A. nom
B. sbt
C. Wheels
D. jars
E. CRAN
F. CRAM

Answer: A

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows

NEW QUESTION # 207
Which of the following is true of Delta Lake and the Lakehouse?

A. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
B. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C. Z-order can only be applied to numeric values stored in Delta Lake tables
D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

Answer: E

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 208
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

Answer: A

Explanation:
Using DataFrame.transform with a pure transformation function promotes modular, reusable, and easily testable ETL logic. Each transformation is encapsulated as a standalone function, can be independently unit tested, and composed cleanly in a pipeline without coupling to orchestration or class state.

NEW QUESTION # 209
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