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A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

• A. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
• B. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
• C. Use Repos to merge all differences and make a pull request back to the remote repository.
• D. Use repos to merge all difference and make a pull request back to the remote repository.
• E. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

• A. SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE ''[0-9-
  +()\\s]+$'
• B. SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
• C. SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
• D. SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
  100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE ''[0-9-+()\\s]+$' THEN 1 END)*
  100.0/COUNT (*)) as phone_invalid_pct FROM customers)

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

• A. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
• B. Merge all changes back to the main branch in the remote Git repository and clone the repo again
• C. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
• D. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
• E. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

• A. End-to-end
• B. Unit
• C. Manual
• D. Integration
• E. functional

A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

• A. New data is automatically clustered during write operations.
• B. New data remains unclustered until the next OPTIMIZE operation.
• C. New data is rejected if it doesn't match the clustering pattern.
• D. New data is written to a staging area and clustered during scheduled maintenance.