A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

1. A. Merge
2. B. Push
3. C. Pull
4. D. Commit
5. E. Clone

Correct Answer: C
From the docs:
In Databricks Repos, you can use Git functionality to: Clone, push to, and pull from a remote Git repository.
Create and manage branches for development work, including merging, rebasing, and resolving conflicts.
Create notebooks—including IPYNB notebooks—and edit them and other files.
Visually compare differences upon commit and resolve merge conflicts. Source: https://docs.databricks.com/en/repos/index.html

Which of the following commands will return the number of null values in the member_id column?

1. A. SELECT count(member_id) FROM my_table;
2. B. SELECT count(member_id) - count_null(member_id) FROM my_table;
3. C. SELECT count_if(member_id IS NULL) FROM my_table;
4. D. SELECT null(member_id) FROM my_table;
5. E. SELECT count_null(member_id) FROM my_table;

Correct Answer: C
https://docs.databricks.com/en/sql/language-manual/functions/count.html
Returns
A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL. If DISTINCT duplicate rows are not counted.

A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

1. A. CREATE TABLE all_transactions AS SELECT * FROM march_transactionsINNER JOIN SELECT * FROM april_transactions;
2. B. CREATE TABLE all_transactions AS SELECT * FROM march_transactions UNION SELECT * FROM april_transactions;
3. C. CREATE TABLE all_transactions AS SELECT * FROM march_transactionsOUTER JOIN SELECT * FROM april_transactions;
4. D. CREATE TABLE all_transactions AS SELECT * FROM march_transactionsINTERSECT SELECT * from april_transactions;
5. E. CREATE TABLE all_transactions AS SELECT * FROM march_transactions MERGE SELECT * FROM april_transactions;

Correct Answer: B
To create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records, you should use the UNION operator, as shown in option B. This operator combines the result sets of the two tables while automatically removing duplicate records.

A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

1. A. pyspark.sql.types.DateType
2. B. datetime
3. C. pyspark.sql.types.TimestampType
4. D. Cron syntax
5. E. There is no way to represent and submit this information programmatically

Correct Answer: D

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?

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

Correct Answer: B
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.