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

Section	Weight	Objectives
Data Transformation, Cleansing, and Quality	~12%	- Enforce data quality and quarantine bad data - Apply advanced Spark transformations
Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Developing Code for Data Processing using Python and SQL	~22%	- Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures
Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage

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Databricks Certified Data Engineer Professional Sample Questions (Q185-Q190):

NEW QUESTION # 185
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.

Answer: D

NEW QUESTION # 186
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A. A job cluster configured to autoscale across multiple workers during the pipeline run.
B. A high-concurrency cluster designed for interactive SQL workloads.
C. An all-purpose cluster always kept running to ensure low-latency job startup times.
D. A lightweight single-node cluster with low worker node count to reduce costs.

Answer: A

Explanation:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.

NEW QUESTION # 187
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. Unit
B. functional
C. Manual
D. Integration
E. End-to-end

Answer: A

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

NEW QUESTION # 188
Which statement describes a key benefit of an end-to-end test?

A. It makes it easier to automate your test suite
B. It closely simulates real world usage of your application.
C. It pinpoint errors in the building blocks of your application.
D. It provides testing coverage for all code paths and branches.

Answer: B

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.

NEW QUESTION # 189
When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

A. The group needs to have USE CATALOG and USE SCHEMA on the catalog.
B. The group should be made OWNER of the catalog.
C. The group needs to have ALL PRIVILEGES on the catalog.
D. The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.

Answer: A

Explanation:
Granting the project group USE CATALOG (and USE SCHEMA on existing schemas) allows team members to work within the catalog without giving them ownership or administrative control.
This enables teams to create and manage schemas, tables, and volumes inside the catalog while preventing them from renaming, deleting, or modifying catalog-level permissions, which remain under central IT governance.

NEW QUESTION # 190
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