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Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer is a certification exam that tests the skills and knowledge required to design and implement data solutions using Databricks. Databricks is a cloud-based data platform that helps organizations manage and process large amounts of data. Databricks Certified Professional Data Engineer Exam certification exam is designed for data engineers who are responsible for creating and maintaining data pipelines, managing data storage, and implementing data solutions.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
A new user who currently does not have access to the catalog or schema is requesting access to the customer table in sales schema, but the customer table contains sensitive information, so you have decided to create view on the table excluding columns that are sensitive and granted access to the view GRANT SELECT ON view_name to [email protected] but when the user tries to query the view, gets the error view does not exist. What is the issue preventing user to access the view and how to fix it?

A. User needs ADMIN privilege on the view
B. User requires SELECT on the underlying table
C. User has to be the owner of the view
D. User requires USAGE privilege on Sales schema
E. User requires to be put in a special group that has access to PII data

Answer: D

Explanation:
Explanation
The answer is User requires USAGE privilege on Sales schema,
Data object privileges - Azure Databricks | Microsoft Docs
GRANT USAGE ON SCHEMA sales TO [email protected];
*USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.

NEW QUESTION # 45
Projecting a multi-dimensional dataset onto which vector has the greatest variance?

A. second principal component
B. first principal component
C. second eigenvector
D. first eigenvector
E. not enough information given to answer

Answer: B

Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.

NEW QUESTION # 46
The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. The RDD Block Name included the '' annotation signaling failure to cache
B. On Heap Memory Usage is within 75% of off Heap Memory usage
C. Size on Disk is> 0
D. The number of Cached Partitions> the number of Spark Partitions

Answer: A

Explanation:
In the Spark UI's Storage tab, an indicator that a cached table is not performing optimally would be the presence of the _disk annotation in the RDD Block Name. This annotation indicates that some partitions of the cached data have been spilled to disk because there wasn't enough memory to hold them. This is suboptimal because accessing data from disk is much slower than from memory. The goal of caching is to keep data in memory for fast access, and a spill to disk means that this goal is not fully achieved.

NEW QUESTION # 47
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

A. Define and import unit test functions from a separate Databricks notebook
B. Define and unit test functions using Files in Repos
C. Define units test and functions within the same notebook
D. Run unit tests against non-production data that closely mirrors production

Answer: D

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
References:
* Databricks Documentation on Testing: Testing and Validation of Data and Notebooks

NEW QUESTION # 48
A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?

A. They can set up the data to stream from the first Job to the second Job
B. They can utilize multiple tasks in a single job with a linear dependency
C. They can use cluster pools to help the Jobs run more efficiently
D. They can set up a retry policy on the first Job to help it run more quickly
E. They can limit the size of the output in the second Job so that it will not fail as easily

Answer: B

NEW QUESTION # 49
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