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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q68-Q73):

NEW QUESTION # 68
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

Answer: D

Explanation:
To ensure a service principal can query data in two specific catalogs while maintaining the principle of least privilege, you must assign the following permissions for each catalog:
Unified Catalog Permissions
USE CATALOG on the specific catalog: This is a prerequisite that allows the principal to see the catalog and its child objects.
USE SCHEMA on the schemas containing the data: This allows the principal to interact with the schemas within that catalog.
SELECT on the specific tables or views: This provides the actual read access required to query the data.
Reference:
https://learn.microsoft.com/en-us/purview/data-governance-roles-permissions


NEW QUESTION # 69
Drag and Drop Question
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1.
You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location. The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 70
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?

Answer: D

Explanation:
To incorporate changes from your feature branch into the main branch while keeping every individual commit intact, you must use a fast-forward merge or a standard merge commit.
In the Azure Databricks Git folders UI or via standard Git operations, use the following command:
The Correct Command: git merge <branch-name>
Preserves History: Unlike a squash merge, a standard merge keeps all individual commit messages, authors, and timestamps.
Maintains Timeline: It seamlessly integrates the exact commit graph from your feature branch into the main branch.
Reference:
https://docs.databricks.com/aws/en/repos/git-operations-with-repos


NEW QUESTION # 71
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: 'overwrite' replaces existing data; 'append' adds to it.
Always check whether the code uses the correct Delta format (.format('delta')), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics


NEW QUESTION # 72
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?

Answer: B

Explanation:
The File arrival trigger is exactly what you need for this use case. It natively solves your problem by launching a job the moment new files land in your Azure Storage container, automatically scaling down to zero compute when no data is processing.
Event-Driven: Instead of relying on a time-based schedule (cron) that wastes resources, the Lakeflow Job monitors the specific Azure location and starts exactly when new data appears.
Zero Idle Compute: You can use serverless compute or an ephemeral job cluster that spins up just for the duration of the run and terminates immediately after, meaning zero billing during downtime.
Cloud Cost Efficient: Listing files to detect new arrivals is typically free or fractions of a cent (only incurring minimal cloud provider API costs for listing the storage location).
Reference:
https://learn.microsoft.com/en-us/azure/databricks/jobs/file-arrival-triggers


NEW QUESTION # 73
......

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