Customizable Databricks Databricks-Certified-Data-Engineer-Professional Exam Mode - Databricks-Certified-Data-Engineer-Professional New Braindumps Questions

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

Section	Weight	Objectives
Data Governance	7%	- Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance
Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management
Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Monitoring and Alerting	10%	- Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads
Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs
Data Modelling	6%	- Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models
Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit
Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
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?

A. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
C. The current table schema does not contain the field valid coordinates; schema evolution will need Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from to be enabled before altering the table to add a constraint.
D. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
E. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

Answer: D

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.

NEW QUESTION # 138
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B. No; the change data feed only tracks inserts and updates not deleted records.
C. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
E. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Answer: A

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 139
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?

A. CAN ATTACH TO
B. CAN RESTART
C. CAN VIEW
D. CAN MANAGE

Answer: C

Explanation:
The CAN VIEW permission on a cluster allows users to see cluster details, including driver and executor logs. This is the minimal permission required for the development team to access logs without granting them the ability to modify, restart, or attach notebooks to the cluster.

NEW QUESTION # 140
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
B. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
C. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
D. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
E. Periodically run OPTIMIZE table_name.

Answer: D,E

Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.

NEW QUESTION # 141
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. Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
B. Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
C. 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.
D. Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.

Answer: C

Explanation:
Packaging dependencies as wheel files and managing them through Databricks-supported artifacts ensures consistent, reproducible environments across interactive and job clusters.
Storing the wheels in Workspace Files or Unity Catalog Volumes complies with no-internet security constraints, while installing them via cluster libraries or Asset Bundles standardizes dependency management across all jobs and pipelines.

NEW QUESTION # 142
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