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Databricks Certified Professional Data Engineer Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
You are working on a marketing team request to identify customers with the same information between two tables CUSTOMERS_2021 and CUSTOMERS_2020 each table contains 25 columns with the same schema, You are looking to identify rows that match between two tables across all columns, which of the following can be used to perform in SQL

A. 1.SELECT * FROM CUSTOMERS_2021
2.EXCEPT
3.SELECT * FROM CUSTOMERS_2020
B. 1.SELECT * FROM CUSTOMERS_2021
2. UNION ALL
3.SELECT * FROM CUSTOMERS_2020
C. 1.SELECT * FROM CUSTOMERS_2021
2. UNION
3.SELECT * FROM CUSTOMERS_2020
D. 1.SELECT * FROM CUSTOMERS_2021 C1
2.INNER JOIN CUSTOMERS_2020 C2
3.ON C1.CUSTOMER_ID = C2.CUSTOMER_ID
E. 1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020

Answer: E

Explanation:
Explanation
Answer is,
1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020
To compare all the rows between both the tables across all the columns using intersect will help us achieve that, an inner join is only going to check if the same column value exists across both the tables on a single column.
INTERSECT [ALL | DISTINCT]
*Returns the set of rows which are in both subqueries.
If ALL is specified a row that appears multiple times in the subquery1 as well as in subquery will be returned multiple times.
If DISTINCT is specified the result does not contain duplicate rows. This is the default.

NEW QUESTION # 124
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Date
B. Post_time
C. Post_id
D. User_id

Answer: A

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned.
Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
Reference:
Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning

NEW QUESTION # 125
Which of the following describes a scenario in which a data engineer will want to use a Job cluster instead of
an all-purpose cluster?

A. A data engineer needs to manually investigate a production error
B. An ad-hoc analytics report needs to be developed while minimizing compute costs
C. A Databricks SQL query needs to be scheduled for upward reporting
D. A data team needs to collaborate on the development of a machine learning model
E. An automated workflow needs to be run every 30 minutes

Answer: E

NEW QUESTION # 126
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 127
Which is a key benefit of an end-to-end test?

A. It pinpoint errors in the building blocks of your application.
B. It closely simulates real world usage of your application.
C. It provides testing coverage for all code paths and branches.
D. It makes it easier to automate your test suite

Answer: B

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
References:
* Software Testing: End-to-End Testing

NEW QUESTION # 128
......

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