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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which distribution does Databricks support for installing custom Python code packages?

A. jars
B. CRAM
C. CRAN
D. nom
E. Wheels
F. sbt

Answer: D

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows

NEW QUESTION # 22
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. await("event_time + `10 minutes'")
B. awaitArrival("event_time", "10 minutes")
C. slidingWindow("event_time", "10 minutes")
D. delayWrite("event_time", "10 minutes")
E. withWatermark("event_time", "10 minutes")

Answer: E

Explanation:
This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming.

NEW QUESTION # 23
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will succeed; the string "redacted" will be printed.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. The connection to the external table will fail; the string "redacted" will be printed.
E. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.

Answer: A

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

NEW QUESTION # 24
A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

Answer: A

Explanation:
This approach first aggregates sales by salesperson and region to compute total cumulative sales. It then applies a window function partitioned by region and ordered by total sales in descending order, using dense_rank to assign ranks so that salespeople with equal totals share the same rank and the highest total receives rank 1.

NEW QUESTION # 25
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 MANAGE
B. CAN RESTART
C. CAN ATTACH TO
D. CAN VIEW

Answer: D

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 # 26
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