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Real Databricks Databricks-Certified-Data-Engineer-Associate Questions - Verified By Experts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: D

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NEW QUESTION # 25
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would be able to collaborate on projects in real-time
B. Both teams would reorganize to report to the same department
C. Both teams would use the same source of truth for their work
D. Both teams would autoscale their work as data size evolves
E. Both teams would respond more quickly to ad-hoc requests

Answer: C

NEW QUESTION # 26
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos provides the ability to comment on specific changes
B. Databricks Repos automatically saves development progress
C. Databricks Repos supports the use of multiple branches
D. Databricks Repos allows users to revert to previous versions of a notebook
E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Answer: C

Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.

NEW QUESTION # 27
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can turn on the Serverless feature for the SQL endpoint.
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
D. They can increase the cluster size of the SQL endpoint.
E. They can increase the maximum bound of the SQL endpoint's scaling range.

Answer: E

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-q

NEW QUESTION # 28
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(availableNow=True)
B. trigger(parallelBatch=True)
C. trigger(continuous="once")
D. processingTime(1)
E. trigger(processingTime="once")

Answer: A

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter

NEW QUESTION # 29
......

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