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Pass Guaranteed 2025 Databricks Databricks-Certified-Professional-Data-Engineer Perfect Testking Learning Materials

Pass Guaranteed 2025 Databricks Databricks-Certified-Professional-Data-Engineer Perfect Testking Learning Materials

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Databricks Certified Professional Data Engineer certification exam is an important credential for data engineers who work with Databricks. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidate's ability to design, build, and maintain data pipelines, as well as their knowledge of various data engineering tools and techniques. Databricks Certified Professional Data Engineer Exam certification can enhance the holder's career prospects and demonstrate their proficiency in using Databricks for data engineering tasks.

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Databricks Certified Professional Data Engineer exam is a certification program designed for data professionals who want to validate their expertise in building and maintaining data pipelines using Databricks. Databricks is a cloud-based data engineering platform that provides a unified analytics engine for big data processing, machine learning, and streaming analytics. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and optimize data pipelines using Databricks, as well as their proficiency in data modeling, data warehousing, and data integration.

Databricks Certified Professional Data Engineer certification is recognized by the industry as a standard for measuring the skills and expertise of data engineers who work with Databricks. It demonstrates that the candidate has the knowledge and skills required to design and build data solutions using Databricks that meet the highest standards of performance, scalability, and reliability. Databricks Certified Professional Data Engineer Exam certification is valuable for data engineers who want to advance their careers by demonstrating their proficiency in using Databricks and for organizations that want to ensure that their data engineers have the skills and expertise needed to build and maintain data solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
A Delta Lake table was created with the below query:
Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

  • A. The table reference in the metastore is updated and all data files are moved.
  • B. The table reference in the metastore is updated and no data is changed.
  • C. A new Delta transaction log Is created for the renamed table.
  • D. All related files and metadata are dropped and recreated in a single ACID transaction.
  • E. The table name change is recorded in the Delta transaction log.

Answer: B

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.


NEW QUESTION # 119
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch nameddev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

  • A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
  • B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-
    2.3.9
  • C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
  • D. Merge all changes back to the main branch in the remote Git repository and clone the repo again
  • E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

Answer: A

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-
2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.


NEW QUESTION # 120
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex,
Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of
the clusters, you notice that there is significant overlap between the clusters. What should you do?

  • A. Decrease the number of clusters
  • B. Remove one of the measures
  • C. Increase the number of clusters
  • D. Identify additional measures to add to the analysis

Answer: A


NEW QUESTION # 121
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

  • A. Reduce the size of the SQL Cluster size
  • B. Reduce the max size of auto scaling from 10 to 5
  • C. Change the spot instance policy from reliability optimized to cost optimized
  • D. Always use X-small cluster
  • E. Setup the dashboard refresh schedule to end in two weeks

Answer: E

Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks


NEW QUESTION # 122
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

  • A. The tables should be refreshed in the writing cluster before the next query is run
  • B. The tables should be altered to include metadata to not cache
  • C. The tables should be updated before the next query is run
  • D. The tables should be stored in a cloud-based external system
  • E. The tables should be converted to the Delta format

Answer: E


NEW QUESTION # 123
......

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