Quiz 2026 Databricks Valid Exam Databricks-Certified-Professional-Data-Engineer Simulations

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

SectionObjectives
Topic 1: Data Ingestion and Transformation- Ingest data using Apache Spark and Databricks
- Handle batch and streaming data pipelines
- Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 2: Security, Governance, Monitoring, and Optimization- Cost optimization and performance tuning
- Monitor and optimize Spark workloads
- Implement Unity Catalog governance and access control
Topic 3: Data Modeling and Storage- Delta Lake table design and optimization
- Schema evolution and data partitioning strategies
- Design scalable data lakehouse architectures
Topic 4: Production Pipelines and Orchestration- Build and manage workflows using Databricks Jobs
- Automate ETL pipelines and scheduling
- Pipeline reliability and fault tolerance

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.

Answer: C

Explanation:
Explanation
Answer is Databricks Notebooks support real-time coauthoring on a single notebook Every change is saved, and a notebook can be changed my multiple users.


NEW QUESTION # 37
Which of the following tool provides Data Access control, Access Audit, Data Lineage, and Data discovery?

Answer: E


NEW QUESTION # 38
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

Answer: E

Explanation:
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active. Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html


NEW QUESTION # 39
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

Answer: E

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint


NEW QUESTION # 40
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schem a. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

Answer: E

Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Checkpointing" section.


NEW QUESTION # 41
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