Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions : Databricks Certified Data Engineer Professional Exam

  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Jul 26, 2026
  • Q&As: 250 Questions and Answers

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

SectionObjectives
Topic 1: Data Modeling and Transformation- Performance optimization techniques
- Spark SQL transformations
- Dimensional modeling concepts
Topic 2: Delta Lake and Data Management- Delta Lake transactions and ACID properties
- Time travel and versioning
- Schema evolution and enforcement
Topic 3: Databricks Lakehouse Platform Architecture- Medallion architecture (Bronze, Silver, Gold)
- Data governance concepts (Unity Catalog basics)
- Workspace and cluster architecture
Topic 4: Data Ingestion and Processing- ETL pipeline design patterns
- Batch and streaming ingestion with Auto Loader
- Structured Streaming fundamentals
Topic 5: Production Pipelines and Orchestration- Databricks Workflows
- Error handling and recovery strategies
- Job scheduling and monitoring

Databricks Certified Data Engineer Professional Sample Questions:

1. A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A) The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
B) Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
C) The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
D) Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.


2. A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?

A) Create a Python wheel file for the entire project, upload it to the Databricks Workspace Files or Volumes, and install it using a Cluster Library or pip install in a requirements.txt declared within a Databricks Asset Bundle.
B) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
C) Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
D) Use an init script on the cluster to install all dependencies using pip, referencing the local file system.


3. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Task queueing resulting from improper thread pool assignment.
B) Spill resulting from attached volume storage being too small.
C) Skew caused by more data being assigned to a subset of spark-partitions.
D) Network latency due to some cluster nodes being in different regions from the source data
E) Credential validation errors while pulling data from an external system.


4. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
B) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
E) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.


5. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
D) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
E) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: E
Question # 5
Answer: D

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