HOT New Databricks-Certified-Data-Engineer-Associate Practice Questions: Databricks Certified Data Engineer Associate Exam - Trustable Databricks Databricks-Certified-Data-Engineer-Associate Latest Test Cram

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. dbfs:/user/hive/database
B. dbfs:/user/hive/warehouse
C. dbfs:/user/hive/database/customer360
D. More information is needed to determine the correct response
E. dbfs:/user/hive/customer360

Answer: B

NEW QUESTION # 71
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. DELETE FROM my_table WHERE age > 25;
B. UPDATE my_table WHERE age > 25;
C. DELETE FROM my_table WHERE age <= 25;
D. SELECT * FROM my_table WHERE age > 25;
E. UPDATE my_table WHERE age <= 25;

Answer: A

NEW QUESTION # 72
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. CREATE TABLE AS SELECT statements cannot be used on files
B. Parquet files have a well-defined schema
C. Parquet files will become Delta tables
D. Parquet files have the ability to be optimized
E. Parquet files can be partitioned

Answer: B

Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.

NEW QUESTION # 73
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution. Which tool should the data engineer use to inspect the code execution and variables in real-time?

A. SQL Analytics
B. Cluster Logs
C. Job Execution Dashboard
D. Python Notebook Interactive Debugger

Answer: D

Explanation:
The Python Notebook Interactive Debugger in Databricks allows stepping through code execution, setting breakpoints, and inspecting variable values in real-time. This makes it the correct tool for debugging Python code inside a Databricks notebook.

NEW QUESTION # 74
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log. Which command should the Data Engineer use to achieve this?
(Choose two.)

A. SELECT * FROM students@v4
B. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T14:32:58.000+00:00'
C. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T14:32:47.000+00:00'
D. SELECT * FROM students FROM HISTORY VERSION AS OF 3
E. SELECT * FROM students VERSION AS OF 5

Answer: A,C

Explanation:
The Data Engineer wants to query the students table as it existed before the UPDATE operation.
Based on the log, the UPDATE operation happened at version 5, with the timestamp 2024-04-
22T14:32:58.000+00:00. Therefore, the table needs to be queried as it existed at version 4 or earlier, which corresponds to before the update operation.
SELECT * FROM students@v4: This will retrieve the table as it was at version 4, which is before the update, so this is correct.
SELECT * FROM students TIMESTAMP AS OF '2024-04-22T14:32:47.000+00:00': This retrieves the table as it existed before the update, using the timestamp of version 4, so this is also correct.

NEW QUESTION # 75
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