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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 2: Prepare and process data | 30-35% | - Data transformation and modeling
|
| Topic 3: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 4: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
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NEW QUESTION # 28
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
metastore1 contains:
* A catalog named Sales
* A schema named Customers in the Sales catalog
* A table named Customer_details in the Customers schema
You need to ensure that a user named User1 can update the data in Customer_details. The solution must meet the following requirements:
* Ensure that User1 cannot create new tables.
* Follow the principle of least privilege.
Which permission should you grant to User1 for each object? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
User1 requires permissions at each level of the Unity Catalog hierarchy. USE CATALOG allows User1 to interact with objects contained in the Sales catalog. USE SCHEMA permits access to objects within the Customers schema. Neither permission independently grants access to the data.
At the table level, MODIFY permits User1 to insert, update, and delete data in Customer_details. It does not grant permission to create new tables. The CREATE TABLE privilege is deliberately not granted on the Customers schema, ensuring that User1 cannot create tables. MANAGE would provide unnecessarily broad administrative capabilities, including the ability to manage permissions, and would violate the principle of least privilege. SELECT provides read access but does not authorize updates. Azure Databricks Therefore, the minimum permissions available in the answer area are USE CATALOG on Sales, USE SCHEMA on Customers, and MODIFY on Customer_details.
NEW QUESTION # 29
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: B
NEW QUESTION # 30
You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Enable audit logging for: The Databricks account
Send audit logs to: Log Analytics
Enabling audit logging at the Databricks account level provides centralized coverage of account and workspace activities, including Unity Catalog data-access events. Enabling logging only for Workspace1 would provide a narrower configuration and would not offer the same centralized account-level auditing model. Individual Unity Catalog tables and the Azure subscription are not the appropriate Databricks audit- log scope selections. Log Analytics is the suitable destination because it supports rapid ingestion and immediate querying of log records through Azure Monitor Logs. Auditors can search, filter, correlate, and analyze the captured events without first developing a separate data-processing solution. An Azure Storage account is more suitable for inexpensive archival, while Event Hubs is primarily a streaming transport that normally requires an additional consumer before investigators can analyze the records.
NEW QUESTION # 31
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
Answer: A
Explanation:
The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn
NEW QUESTION # 32
You use Databricks Asset Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements:
- Deploy the app to both environments.
- Deploy only one job to development.
- Minimize administrative effort.
What should you use?
Answer: C
Explanation:
To meet your deployment needs with minimal administrative overhead, you should use Databricks Asset Bundle (DAB) targets combined with resource overrides or conditional lookups using Go template syntax in your databricks.yml file.
Instead of creating separate bundles or complex CI/CD logic, you can declare both jobs and the app inside the main resources block, and then filter or modify them per target environment.
Implementation Options
Depending on your preference for keeping configuration declarative or fully conditional, choose one of the two standard patterns:
*-> Option 1: Resource Overrides via targets (Recommended)
Define all components globally, but use the targets block to remove or empty out the configuration of the job you want to skip in development.yamlbundle:
name: my-unified-bundle
apps:
my_app:
# App configurations go here...
jobs:
job_shared:
# Configurations for the job deployed to BOTH dev and prod...
job_prod_only:
name: Only for Production
# Production configurations...
targets:
dev:
mode: development
workspace:
host: https://dev-workspace.cloud.databricks.com
# Keep administrative work low by overriding the prod job to null/empty resources:
jobs:
job_prod_only: !null
prod:
mode: production
workspace:
host: https://prod-workspace.cloud.databricks.com
Option 2: Go Template Conditional LogicIf you prefer not to use !null overrides, you can evaluate the target environment dynamically at deployment time using standard Go templates.
Reference:
https://docs.databricks.com/aws/en/dev-tools/bundles/workspace-deploy
NEW QUESTION # 33
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