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Certified-Data-Engineer-Professional pdf
  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Sep 04, 2026
  • Q & A: 250 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Sep 04, 2026
  • Q & A: 250 Questions and Answers
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  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

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 on bpm_stats
B. Set the pipelines, reset, allowed property to false on raw_iot
C. Set the SkipChangeCommits flag to true raw_lot
D. Set the pipelines, reset, allowed property to false on bpm_stats


Question 2

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. New fields cannot be added to a production Delta table.
B. Spark cannot capture the topic partition fields from the kafka source.
C. Updating the table schema will invalidate the Delta transaction log metadata.
D. Updating the table schema requires a default value provided for each file added.
E. New fields will not be computed for historic records.


Question 3

A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

A. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.
B. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.
C. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
D. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.


Question 4

A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
B. Use an Auto CDC pipeline with batch tables to simplify late data handling.
C. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
D. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.


Question 5

The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Configure a job that executes every time new data lands in a given directory.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Schedule a job to execute the pipeline once an hour on a new job cluster.
D. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.


Solutions:

Question 1
Answer: B
Question 2
Answer: E
Question 3
Answer: C
Question 4
Answer: C
Question 5
Answer: C

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