DP-750 Exam Dumps - Implementing Data Engineering Solutions Using Azure Databricks

The Analysts group can query the Sales table because it has all three privileges required by the Unity Catalog hierarchy: USE CATALOG on Corpdb, USE SCHEMA on Finance, and SELECT on Sales. These permissions allow the group to navigate through the catalog and schema and read the table. However, USE SCHEMA does not grant permission to create tables. Creating a table in Finance would additionally require CREATE TABLE on that schema. Similarly, USE CATALOG only permits access to the catalog; it does not permit schema creation. Creating a schema in Corpdb would require the CREATE SCHEMA privilege on the catalog. Therefore, only the first statement is true.

The correct answer is A — merge.

A Git merge combines the histories of two branches by creating a merge commit that joins them. Every individual commit from Branch1 remains visible in the repository log — the full development history is preserved. This is the requirement: ' the solution must preserve the commit history in the repository. '

Option C (rebase) moves Branch1 ' s commits on top of main by replaying them as new commits with new hashes. The end result looks like a linear history, but the original commit hashes are rewritten — the prior history is not preserved in its original form. For a shared repository, rebase rewrites public history, which is considered problematic.

Option B (pull) fetches remote changes and merges or rebases them into the current branch — it ' s used to sync with a remote, not to incorporate a feature branch. Option D (push) sends local commits to the remote but doesn ' t incorporate any branch into another.

Branch1 must first incorporate the current changes from main. During that merge, the conflicting sections of Notebook1 can be reviewed and resolved so that the resulting feature-branch version contains the required work from both branches. After committing the resolution, Branch1 can be merged into main normally. Cloning either branch into another Git folder creates another working copy but does not resolve the conflicting histories. Applying changes directly to main bypasses the controlled feature-branch workflow and risks omitting or overwriting one contributor’s work. Resolving the conflict on Branch1 also allows the combined notebook to be tested before updating the shared main branch. Therefore, merging main into Branch1, resolving the conflict, and completing the final merge is the correct workflow.

Two things are needed here:

For the rule enforcement: use @dlt.expect_or_drop. This drops any record that violates rule1 before it reaches Table1, while the pipeline continues processing all valid records. The table only ever receives clean data.

For reviewing metrics: the Lakeflow SDP Pipeline UI is the right tool — zero development effort required. The pipeline graph shows expectation pass/fail counts directly on each table node, and the event log provides a detailed per-batch breakdown of how many records were dropped and why. Data engineers can inspect this at any time without writing additional monitoring queries or connecting external dashboards.

This combination is one of the strongest arguments for SDP over hand-coded Structured Streaming: expectation observability is built in, not bolted on.