Databricks Databricks-Certified-Data-Engineer-Professional exam dumps - Databricks Certified Data Engineer Professional Exam

  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Sep 12, 2026     Q & A: 250 Questions and Answers

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

SectionObjectives
Databricks Lakehouse Platform Architecture- Workspace and cluster architecture
- Medallion architecture (Bronze, Silver, Gold)
- Data governance concepts (Unity Catalog basics)
Production Pipelines and Orchestration- Databricks Workflows
- Job scheduling and monitoring
- Error handling and recovery strategies
Data Modeling and Transformation- Spark SQL transformations
- Performance optimization techniques
- Dimensional modeling concepts
Delta Lake and Data Management- Delta Lake transactions and ACID properties
- Schema evolution and enforcement
- Time travel and versioning
Data Ingestion and Processing- Batch and streaming ingestion with Auto Loader
- ETL pipeline design patterns
- Structured Streaming fundamentals

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

  • A. Reduce spark.sql.files.maxPartitionBytes.
  • B. Choose a compute instance with a higher core-to-memory ratio.
  • C. Increase spark.sql.files.maxPartitionBytes.
  • D. Choose a compute instance with more disk space.
  • E. Choose a compute instance with more network bandwidth.
Reveal Solution  Discussion  0

Correct Answer: A,B  🗳️

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Question #2

A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

  • A. Date
  • B. User_id
  • C. Post_time
  • D. latitude
  • E. Post_id
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

  • A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
  • B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
  • C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
  • D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
  • E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

  • A. import dlt
    @dlt.table
    def raw_customers():
    return spark.read.json("s3://my-bucket/raw-customers/")
  • B. import dlt
    @dlt.table
    def raw_customers():
    return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
  • C. import dlt
    @dlt.table
    def raw_customers():
    return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
  • D. import dlt
    @dlt.view
    def raw_customers():
    return spark.format.json("s3://my-bucket/raw-customers/")
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

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. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
  • B. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
  • C. Use an Auto CDC pipeline with batch tables to simplify late data handling.
  • D. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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