The Databricks Certified Data Engineer Professional exam validates your ability to design, build, and maintain data pipelines on the Databricks platform. This certification is intended for engineers with hands-on experience in data processing, modeling, and governance who want to demonstrate expertise in the Data Engineer Professional path. This page outlines the exam syllabus, question formats, and effective preparation strategies to help you succeed.
Use this topic map to guide your study for Databricks Databricks-Certified-Professional-Data-Engineer (Databricks Certified Data Engineer Professional) within the Data Engineer Professional path.
The exam uses multiple question formats to assess both conceptual knowledge and practical decision-making in real-world scenarios.
Questions progress in difficulty and reflect the complexity of production data engineering work on Databricks.
An effective study plan maps the six core topics to a structured timeline, with regular practice and review to reinforce connections between concepts. Allocate study time proportionally to topic weight and your own knowledge gaps.
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Data Processing and Security and Governance typically account for a larger portion of the exam. However, all six domains are tested, so balanced preparation across Databricks Tooling, Data Modeling, Monitoring and Logging, and Testing and Deployment is essential for a strong score.
In practice, these domains are interdependent. You use Databricks Tooling to build pipelines that apply Data Processing and Data Modeling logic, while Security and Governance controls who can access the data. Monitoring and Logging tracks pipeline health, and Testing and Deployment ensures code quality before production. Understanding these connections helps you answer scenario-based questions more accurately.
The exam is designed for engineers with at least six months of practical experience building data pipelines on Databricks or similar platforms. Hands-on labs focusing on cluster configuration, Delta Lake operations, and job scheduling are especially valuable for reinforcing exam concepts.
Many candidates underestimate Security and Governance topics and focus too heavily on Data Processing. Others miss questions by not reading scenario details carefully or by confusing similar features. Reviewing explanations for practice test errors and revisiting weak topics in the final week helps avoid these pitfalls.
In your last week, take a full-length timed practice test to identify remaining gaps. Spend 60 percent of remaining study time on weak domains and 40 percent reviewing high-confidence areas to maintain retention. Avoid learning new topics; instead, reinforce understanding through targeted practice questions and explanation reviews.
What is the first of a Databricks Python notebook when viewed in a text editor?
When viewing a Databricks Python notebook in a text editor, the first line indicates the format and source type of the notebook. The correct option is % Databricks notebook source, which is a magic command that specifies the start of a Databricks notebook source file.
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
CAN ATTACH TO:
Allows users to attach notebooks to a cluster but does not allow them to view logs.
Not sufficient for viewing driver logs.
CAN MANAGE:
Grants full control over the cluster, including starting, stopping, and editing configurations.
Too broad for this requirement.
CAN VIEW (Correct Answer):
Allows users to view cluster details, logs, and status but not modify any configurations.
Minimal required permission for viewing logs.
CAN RESTART:
Grants permission to restart the cluster, but does not include log access.
Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
Databricks Cluster Permissions Documentation
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling.
How should the data engineer address this issue?
When Spark performs wide transformations such as sortBy or orderBy, large data volumes can exceed memory limits, causing disk spilling. The official Databricks performance tuning guide recommends increasing the shuffle partition count to distribute the data more evenly across executors. By default, Spark uses a fixed number of shuffle partitions (e.g., 200), which can lead to memory imbalance and spill if some partitions are too large. Increasing this number (via spark.sql.shuffle.partitions) results in smaller partitions, reduced in-memory pressure, and improved sort performance. Other options like broadcast joins or single partition sorts do not apply to single-table sorts, and converting to filters changes query logic. Thus, option D is the correct remedy.
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that over time, data being transmitted from the bicycle sensors fails to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({0})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
Databricks documents a quarantine pattern for Lakeflow Spark Declarative Pipelines in which you create a dataset containing both valid and invalid rows, add an is_quarantined flag based on your rule set, and then use that dataset for separate downstream processing paths. The documented pattern uses a boolean quarantine expression and preserves all rows while tracking quality metrics through expectations. (Databricks Documentation)
Option A is the only choice that matches that documented design: it keeps all records, marks invalid rows with is_quarantined, and applies expectations to capture data quality metrics. Option B drops invalid rows, which fails the requirement to keep quarantined records available. Option C captures only bad rows and loses the clean path. Option D also drops invalid rows and therefore does not preserve both good and quarantined records for separate downstream use. (Databricks Documentation)
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A security analytics pipeline must enrich billions of raw connection logs with geolocation data. The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?
The query joins billions of rows (network_events) with millions of rows (ip_ranges) using a range predicate (BETWEEN). Unlike equality joins (=), range joins are not efficiently handled by broadcast or sort-merge joins because:
Broadcast Join (D): Effective for small tables but only for equality joins. Since this query uses a range condition, broadcast will not reduce the complexity of scanning billions of records across non-equality conditions.
Sort-Merge Join (C): Works for ordered joins but is inefficient on range conditions. Sorting billions of records adds excessive overhead and will not resolve the bottleneck.
Increasing Shuffle Partitions (A): Only spreads out shuffle work but does not address the fundamental inefficiency of range-based lookups at scale.
Range Joins in Spark (RANGE_JOIN hint):
Databricks provides range join optimizations specifically for conditions such as BETWEEN. By applying a RANGE_JOIN hint, Spark can build optimized data structures (such as interval indexes or partition pruning strategies) that map billions of input rows to ranges much faster. This avoids brute-force scans and unnecessary shuffle costs.
Thus, Option B is the correct solution because:
It leverages range-join optimization, which is purpose-built for queries joining massive event logs to smaller lookup tables with IP ranges.
This ensures Spark can evaluate billions of rows against millions of ranges with optimized matching logic, drastically improving query performance while preserving correctness.
