Latest Databricks-Certified-Professional-Data-Engineer Guide Files & Databricks-Certified-Professional-Data-Engineer Simulation Questions

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Databricks Certified Professional Data Engineer certification exam is a valuable credential for data engineers who work with Databricks. It demonstrates to potential employers and clients that the holder has the necessary skills and knowledge to work effectively with the platform. Additionally, the certification can enhance the holder's career prospects by providing opportunities for advancement and higher-paying positions.

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Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who work with the Databricks platform. It validates their skills and expertise and demonstrates to employers that they have the knowledge and experience needed to work with Databricks effectively. By passing the exam and earning the certification, data engineers can enhance their career prospects and gain a competitive advantage in the job market.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
B. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
C. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
D. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
E. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

Answer: C

Explanation:
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.

NEW QUESTION # 99
Where are Interactive notebook results stored in Databricks product architecture?

A. Data plane
B. Databricks web application
C. Control plane
D. JDBC data source
E. Data and Control plane

Answer: E

Explanation:
Explanation
The answer is Data and Control plane,
Only Job results are stored in Data Plane(your storage), Interactive notebook results are stored in a combination of the control plane (partial results for presentation in the UI) and customer storage.
https://docs.microsoft.com/en-us/azure/databricks/getting-started/overview#--high-level-architecture Snippet from the above documentation, Graphical user interface, application Description automatically generated

How to change this behavior?
You can change this behavior using Workspace/Admin Console settings for that workspace, once enabled all of the interactive results are stored in the customer account(data plane) except the new notebook visualization feature Databricks has recently introduced, this still stores some metadata in the control pane irrespective of the below settings. please refer to the documentation for more details.
Graphical user interface, text, application, email Description automatically generated

Why is this important to know?
I recently worked on a project where we had to deal with sensitive information of customers and we had a security requirement that all of the data need to be stored in the data plane including notebook results.

NEW QUESTION # 100
Which is a key benefit of an end-to-end test?

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

Answer: C

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.
References:
* Software Testing: End-to-End Testing

NEW QUESTION # 101
Which of the following data workloads will utilize a Bronze table as its destination?

A. A job that ingests raw data from a streaming source into the Lakehouse
B. A job that aggregates cleaned data to create standard summary statistics
C. A job that queries aggregated data to publish key insights into a dashboard
D. A job that enriches data by parsing its timestamps into a human-readable format
E. A job that develops a feature set for a machine learning application

Answer: A

Explanation:
Explanation
The answer is A job that ingests raw data from a streaming source into the Lakehouse.
The ingested data from the raw streaming data source like Kafka is first stored in the Bronze layer as first destination before it is further optimized and stored in Silver.
Medallion Architecture - Databricks
Bronze Layer:
1. Raw copy of ingested data
2. Replaces traditional data lake
3. Provides efficient storage and querying of full, unprocessed history of data
4. No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 102
What is the underlying technology that makes the Auto Loader work?

A. Structured Streaming
B. Loader
C. DataFrames
D. Delta Live Tables
E. Live DataFames

Answer: A

NEW QUESTION # 103
......

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Latest Databricks-Certified-Professional-Data-Engineer Guide Files & Databricks-Certified-Professional-Data-Engineer Simulation Questions

Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions Are Out: Download And Prepare [2025]

Databricks Certified Professional Data Engineer Exam Sample Questions (Q98-Q103):

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