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Valid Databricks-Certified-Professional-Data-Engineer Exam Cram & Databricks-Certified-Professional-Data-Engineer Exam Topic

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Databricks is a cloud-based platform that provides data engineering, machine learning, and analytics services. The Databricks Certified Professional Data Engineer (DCPDE) exam is designed to test the skills and knowledge of data engineers who are responsible for building data pipelines, managing data infrastructure, and optimizing data processing workflows on the Databricks platform.

Databricks is a powerful data engineering and machine learning platform that is widely used across various industries. To ensure that professionals have the necessary skills and expertise to work with Databricks, the company offers a certification program. One of the certifications offered is the Databricks Certified Professional Data Engineer exam, also known as the Databricks Databricks-Certified-Professional-Data-Engineer Exam.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which of the below SQL commands creates a session scoped temporary view?

A. 1.CREATE OR REPLACE VIEW view_name
2.AS SELECT * FROM table_name
B. 1.CREATE OR REPLACE LOCAL TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
C. 1.CREATE OR REPLACE LOCAL VIEW view_name
2.AS SELECT * FROM table_name
D. 1.CREATE OR REPLACE TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
(Correct)
E. 1.CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name

Answer: D

Explanation:
Explanation
The answer is
1.CREATE OR REPLACE TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
The default temporary view is session scoped, as soon as the session ends or if a notebook is de-tached session scoped temporary view is dropped.

NEW QUESTION # 82
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the Databricks Lakehouse Platform?

A. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
B. Databricks Repos can store the single-source-of-truth Git repository
C. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
D. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
E. Databricks Repos can commit or push code changes to trigger a CI/CD process

Answer: E

Explanation:
Explanation
Answer is Databricks Repos can commit or push code changes to trigger a CI/CD process See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram Description automatically generated

NEW QUESTION # 83
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

A. Model Building
B. Data Preparation
C. Discovery
D. Communicate Results

Answer: B

NEW QUESTION # 84
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Executor's log file, by gripping for "predicate push-down"
B. In the Delta Lake transaction log. by noting the column statistics
C. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
D. In the Query Detail screen, by interpreting the Physical Plan
E. In the Storage Detail screen, by noting which RDDs are not stored on disk

Answer: D

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under
"Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.

NEW QUESTION # 85
Which of the following table constraints that can be enforced on Delta lake tables are supported?

A. Primary key, foreign key, Not Null, Check Constraints
B. Not Null, Check Constraints
C. Default, Not Null, Check Constraints
D. Unique, Not Null, Check Constraints
E. Primary key, Not Null, Check Constraints

Answer: B

Explanation:
Explanation
The answer is Not Null, Check Constraints
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-constraints
* CREATE TABLE events( id LONG,
* date STRING,
* location STRING,
* description STRING
* ) USING DELTA;
ALTER TABLE events CHANGE COLUMN id SET NOT NULL;
ALTER TABLE events ADD CONSTRAINT dateWithinRange CHECK (date > '1900-01-01'); Note: Databricks as of DBR 11.1 added support for Primary Key and Foreign Key when Unity Catalog is enabled but this is for information purposes only these are not actually enforced. You may ask then why are we defining these if they are not enforced, so especially these information constraints are very helpful if you have a BI tool that can benefit from knowing the relationship between the tables, so it will be easy when creating reports/dashboards or understanding the data model when using any Data modeling tool.
Primary and Foreign Key
Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 86
......

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Rick Brown Rick Brown

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