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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 - Data Management - Delta Lake
Topic 2: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Topic 3: Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Topic 4: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Topic 5: Data Processing	28%	- Structured Streaming - Spark SQL - Data Transformation - ETL Pipelines

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
E. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Answer: E

NEW QUESTION # 83
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: 8
50 GB per Executor
20 Cores / Executor
B. Total VMs: 4
100 GB per Executor
40 Cores/Executor
C. Total VMs: 1
400 GB per Executor
160 Cores / Executor
D. Total VMs: 2
200 GB per Executor
80 Cores / Executor

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 84
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
B. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.

Answer: A

Explanation:
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.

NEW QUESTION # 85
Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

A. ANALYZE
B. PARTITION BY
C. OPTIMIZE
D. COMPACT
E. BUCKETING

Answer: A,C

Explanation:
Predictive Optimization automatically runs OPTIMIZE to compact small files and improve data layout, and ANALYZE to collect and refresh table statistics. Together, these operations ensure efficient query planning, better data skipping, and optimized performance and cost for Unity Catalog managed Delta tables.

NEW QUESTION # 86
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A. Share the analysis with the failing notebook owner so that they can fix it quickly.
B. Repair the run with the new parameter, and update the task by adding the missing task parameter.
C. Repair the run with the new parameter.
D. Update the task by adding the missing task parameter, and manually run the job.

Answer: B

Explanation:
The repair run capability in Databricks Jobs allows re-execution of failed tasks without re-running successful ones. When a parameterized job fails due to missing or incorrect task configuration, engineers can perform a repair run to fix inputs or parameters and resume from the failed state.
This approach saves time, reduces cost, and ensures workflow continuity by avoiding unnecessary recomputation. Additionally, updating the task definition with the missing parameter prevents future runs from failing.
Running the job manually (B) loses run context; (C) alone does not prevent recurrence; (D) delays resolution. Thus, A follows the correct operational and recovery practice.

NEW QUESTION # 87
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