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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 2: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 4: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
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NEW QUESTION # 94
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a table named Transactions. Transactions contains the following columns:
- transaction_id
- customer_name
- email_address
- credit_card_number
- transaction_amount
You need to ensure that business analysts can query all the rows in the Transactions table. The solution must meet the following requirements:
- Prevent the analysts from seeing the full values in the email_address and credit_card_number columns.
- Ensure that the analysts can see only the values after the @
character in each email address.
- Ensure that the analysts can see only the last four digits of each
credit card number.
- Enable the analysts to query the table without errors.
- Follow the principle of least privilege.
What should you do?
Answer: C
Explanation:
To protect sensitive customer information while allowing a specific group to run queries, use Unity Catalog's Dynamic Column Masking. This grants the group table access while applying User- Defined Functions (UDFs) to redact the email addresses and credit card numbers dynamically.
Here is the exact SQL implementation to apply:
1. Create the masking SQL UDFs
Define functions to apply the exact partial masking rules requested.
2. Apply the column masks to the tableAlter the transaction table to attach these UDFs to the respective columns.
3. Grant least privilege permissionsGrant the group the SELECT permission on the table, along with the foundational permissions to access the catalog and schema.
Reference:
https://docs.databricks.com/aws/en/data-governance/unity-catalog/filters-and-masks
NEW QUESTION # 95
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table! is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table!
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Answer: A
NEW QUESTION # 96
You have an Azure Databricks workspace named Workspace1.
You have a user named User1 who is a non-admin user in Workspace1.
You need to ensure that User1 can perform the following tasks:
* Provision clusters of any size.
* Run Databricks jobs.
The solution must follow the principle of least privilege.
What should you do? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation:
The Allow unrestricted cluster creation entitlement permits a non-admin user to create fully configurable compute resources rather than limiting the user to clusters governed by assigned compute policies. This enables User1 to provision clusters with the required size and configuration without granting the much broader workspace administrator role. Microsoft Learn The CAN RUN job permission permits User1 to execute the applicable Databricks jobs without allowing the user to edit job settings, delete jobs, change permissions, or perform other administrative operations. It is therefore the least-privileged job permission among the provided options.
The workspace admins role would grant excessive administrative control. Consumer access provides a restricted experience for consuming shared resources and does not authorize unrestricted cluster creation or job execution. The Azure Contributor role operates at the Azure resource-management level and does not grant the required permissions inside the Databricks workspace. Consequently, the unrestricted cluster- creation entitlement and the job-level run permission satisfy the requirements with minimum access.
NEW QUESTION # 97
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Answer: A
NEW QUESTION # 98
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
- The schemas and tables can be queried in Databricks.
- The schemas and tables appear alongside other Unity Catalog objects.
- The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You create a foreign catalog in Catalog Explorer.
You should create a Foreign Catalog using Lakehouse Federation.
Data Copying: Lakehouse Federation queries data directly in the source SQL Server without moving or copying it.
Seamless Integration: The database schemas and tables appear right inside Unity Catalog alongside your other data objects.Real-time Access: It provides immediate access to live SQL Server data.
Incorrect:
* You create a Databricks access connector.
* You create a Lakeflow Connect pipeline and connect it to DB1.
Data Copying: Lakeflow Connect is an ingestion tool that physically replicates and copies data into Databricks-managed storage (Delta tables).
Storage Costs: It violates your requirement to keep data out of Databricks storage.
* You create a new native catalog in Unity Catalog.
Note:
To expose the external SQL Server database in Unity Catalog without copying the data, you must use Lakehouse Federation.
Here are the step-by-step actions you need to take:
1. Create a Connection
Create a securable object in Unity Catalog that specifies the path and credentials to access the SQL Server database.
Go to Catalog Explorer or use SQL.
Select External Data > Connections.
Create a connection using the SQL Server connection details (URL, host, port, and database credentials).
*-> 2. Create a Foreign Catalog
Create a specific type of catalog in Unity Catalog that mirrors the external database.
Use the CREATE FOREIGN CATALOG SQL command or the Catalog Explorer UI.
Link this foreign catalog directly to the connection you created in step 1.
3. Query the DataOnce the foreign catalog is created, Unity Catalog automatically syncs the schemas and tables from SQL Server.
Reference:
https://docs.databricks.com/gcp/en/database-objects/
NEW QUESTION # 99
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