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Databricks Certified Data Engineer Associate Exam covers a wide range of topics, including data engineering fundamentals, data ingestion and processing, data warehousing and data lakes, data transformation and manipulation, and data quality and governance. Databricks-Certified-Data-Engineer-Associate Exam is designed to provide a comprehensive understanding of Databricks and its features, and to ensure that candidates are equipped with the necessary skills to work with Databricks effectively.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. USING CSV
B. None of these lines of code are needed to successfully complete the task
C. USING DELTA
D. FROM "path/to/csv"
E. FROM CSV

Answer: D

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. Reference: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 60
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 Auto Stop feature for the SQL endpoint.
B. They can increase the cluster size of the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range.
D. They can turn on the Serverless feature for the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."

Answer: A

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions- from-query/td-p/36696

NEW QUESTION # 61
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 increase the cluster size of 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.
D. They can increase the maximum bound of the SQL endpoint's scaling range.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."

Answer: D

NEW QUESTION # 62
A data engineer needs to reduce the number of small files generated during streaming ingestion into Delta tables. Small files are causing performance issues during downstream queries. Which Delta Lake feature automatically compacts small files during writes?

A. OPTIMIZE
B. VACUUM
C. CACHE
D. autoCompact

Answer: D

NEW QUESTION # 63
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. None of these lines of code are needed to successfully complete the task
B. FROM "path/to/csv"
C. USING DELTA
D. USING CSV
E. FROM CSV

Answer: D

NEW QUESTION # 64
......

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