Databricks Certified Professional Data Engineer Exam Guide
This Databricks Certified Professional Data Engineer exam focuses on practical knowledge and real-world application scenarios related to the subject area. It evaluates your ability to understand core concepts, apply best practices, and make informed decisions in realistic situations rather than relying solely on memorization.
This page provides a structured exam guide, including exam focus areas, skills measured, preparation recommendations, and practice questions with explanations to support effective learning.
Exam Overview
The Databricks Certified Professional Data Engineer exam typically emphasizes how concepts are used in professional environments, testing both theoretical understanding and practical problem-solving skills.
Skills Measured
- Understanding of core concepts and terminology
- Ability to apply knowledge to practical scenarios
- Analysis and evaluation of solution options
- Identification of best practices and common use cases
Preparation Tips
Successful candidates combine conceptual understanding with hands-on practice. Reviewing measured skills and working through scenario-based questions is strongly recommended.
Practice Questions for Databricks Certified Professional Data Engineer Exam
The following practice questions are designed to reinforce key Databricks Certified Professional Data Engineer exam concepts and reflect common scenario-based decision points tested in the certification.
Question#1
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
A. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
B. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
C. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
D. Merge all changes back to the main branch in the remote Git repository and clone the repo again
E. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.
Verified Reference: [Databricks Certified Data Engineer Professional], under “Databricks Tooling” section; Databricks Documentation, under “Pull changes from a remote repository” section.
Question#2
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id.
A few users have millions of records, causing task skew and long runtimes.
Which technique will fix the skew in this aggregation?
A. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
B. Increase the Spark driver memory and retry.
C. Use reduceByKey instead of groupBy to avoid shuffles.
D. Filter out the skewed users before the aggregation.
Explanation:
Task skew occurs when a small subset of keys holds a disproportionate amount of data, causing certain tasks to process significantly more records than others. Databricks documentation recommends salting as an effective mitigation technique.
Salting introduces a random or calculated prefix to skewed keys, distributing records across multiple partitions and balancing the workload during the shuffle stage. After aggregation, a second pass re-aggregates results by removing the prefix to restore key integrity.
Increasing memory (B) does not resolve distribution imbalance; reduceByKey (C) still triggers
shuffles; and filtering (D) would remove valid business data. Hence, salting is the correct and officially recommended approach to address skew in Spark aggregations.
Question#3
The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
A. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html
The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
Question#5
What is true for Delta Lake?
A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B. Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters.
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.
Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table. These statistics help optimize query performance through data skipping, which allows Databricks to scan only relevant parts of a table.
This feature significantly improves query efficiency, especially when dealing with large datasets.
Why Other Options Are Incorrect:
Option A: Views do not cache the most recent versions of the source table; they are recomputed when queried.
Option C: Z-ORDER can be applied to any data type, including strings, to optimize read performance.
Option D: Delta Lake does not enforce primary or foreign key constraints.
Reference: Delta Lake Optimization
Disclaimer
This page is for educational and exam preparation reference only. It is not affiliated with Databricks, Data Engineer, or the official exam provider. Candidates should refer to official documentation and training for authoritative information.
