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Databricks Certified Professional Data Engineer certification is designed for data engineers who work with the Databricks platform and have a deep understanding of data engineering concepts. Databricks Certified Professional Data Engineer Exam certification exam tests the candidate’s ability to design, build, and maintain data pipelines using Databricks, as well as their knowledge of data modeling, data warehousing, and data governance. Databricks Certified Professional Data Engineer Exam certification is recognized globally and indicates that the candidate has the skills and expertise needed to work with Databricks.

Databricks Certified Professional Data Engineer is a certification exam offered by Databricks for data engineers. Databricks-Certified-Professional-Data-Engineer exam evaluates the ability of a candidate to design and implement data solutions using Databricks. Databricks is a unified data analytics platform that enables data teams to collaborate on data engineering, machine learning, and analytics tasks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and proficiency of data engineers in using Databricks for data engineering tasks.

Databricks Certified Professional Data Engineer certification exam is a valuable certification for data professionals who want to demonstrate their skills in building and managing complex data solutions on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Candidates who pass the exam will have a deep understanding of Databricks technologies and will be able to build and manage scalable and reliable data solutions.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q113-Q118):

NEW QUESTION # 113
Which of the following statement is true about Databricks repos?

A. You cannot create a new branch in Databricks repos
B. Databricks Repos and Notebook versioning are the same features
C. You can approve the pull request if you are the owner of Databricks repos
D. A workspace can only have one instance of git integration
E. Databricks repos allow you to comment and commit code changes and push them to a remote branch

Answer: E

Explanation:
Explanation
See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops Diagram Description automatically generated

NEW QUESTION # 114
A facilities-monitoring team is building a near-real-time PowerBI dashboard off the Delta table device_readings:
Columns:
device_id (STRING, unique sensor ID)
event_ts (TIMESTAMP, ingestion timestamp UTC)
temperature_c (DOUBLE, temperature in °C)
Requirement:
For each sensor, generate one row per non-overlapping 5-minute interval, offset by 2 minutes (e.g., 00:02-00:07, 00:07-00:12, ...).
Each row must include interval start, interval end, and average temperature in that slice.
Downstream BI tools (e.g., Power BI) must use the interval timestamps to plot time-series bars.
Options:

A. SELECT device_id,
event_ts,
AVG(temperature_c) OVER (
PARTITION BY device_id
ORDER BY event_ts
RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
) AS avg_temp_5m
FROM device_readings
WINDOW w AS (window(event_ts, '5 minutes', '2 minutes'));
B. SELECT device_id,
window.start AS bucket_start,
window.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM device_readings
GROUP BY device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes') ORDER BY device_id, bucket_start;
C. SELECT device_id,
date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
D. WITH buckets AS (
SELECT device_id,
window(event_ts, '5 minutes', '2 minutes', '5 minutes') AS win,
temperature_c
FROM device_readings
)
SELECT device_id,
win.start AS bucket_start,
win.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM buckets
GROUP BY device_id, win
ORDER BY device_id, bucket_start;

Answer: D

Explanation:
The correct way to satisfy non-overlapping windows with an offset in Databricks SQL is to use the window function with three parameters: window duration, slide duration, and start offset.
In option A, the function call:
window(event_ts, '5 minutes', '2 minutes', '5 minutes')
creates 5-minute windows that slide every 5 minutes, with a 2-minute offset, which exactly matches the requirement (intervals like 00:02-00:07, 00:07-00:12, ...).
Option B is incorrect because it uses a windowed aggregation with RANGE, which produces overlapping sliding averages, not discrete non-overlapping buckets.
Option C manually constructs bucket boundaries with date_trunc and offsets, but this is brittle and less efficient than the built-in window function.
Option D incorrectly passes four parameters to window but with the wrong ordering (5 minutes, 5 minutes, 2 minutes). This creates a sliding window every 5 minutes with overlap, rather than true non-overlapping shifted windows.
Reference (Databricks SQL Windowing Functions):
Databricks documentation specifies that:
window(time_col, windowDuration, slideDuration, startTime)
produces tumbling or sliding windows. When slideDuration = windowDuration, it produces non-overlapping tumbling windows. The startTime argument allows for offset windows, which is why '2 minutes' ensures alignment at 00:02, 00:07, etc.
Thus, A is the only correct solution as it directly implements non-overlapping, offset-based tumbling windows.

NEW QUESTION # 115
A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?

A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log
B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table
C. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset
D. Records that violate the expectation cause the job to fail
E. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log

Answer: A

NEW QUESTION # 116
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance.
How should the data engineer create the patient_encounters table?

A. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
B. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
C. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
D. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog. Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

NEW QUESTION # 117
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. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
C. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
D. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

Answer: C

Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership: When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema does not automatically extend to ownership or permission of individual tables within that schema.
* Table Permissions: For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access: The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution: As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership (https://docs.databricks.com/security/access-control/workspace-acl.html#permissions

NEW QUESTION # 118
......

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