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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 38
Drag and Drop Question
You are developing a new page for a website that uses Azure Cosmos DB for data storage. The feature uses documents that have the following format:
You must display data for the new page in a specific order. You create the following query for the page:
You need to configure an Azure Cosmos DB policy to support the query.
How should you configure the policy? To answer, drag the appropriate JSON segments to the correct locations. Each JSON segment 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.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: compositeIndexes
Azure Cosmos DB requires a defined composite index for any query utilizing an ORDER BY clause with multiple properties.
Box 2: descending
In an ORDER BY clause, properties without an explicit direction default to ascending order. Your query ORDER BY p.name, p.city DESC translates to an ascending sort on /name and a descending sort on /city.
Reference:
https://docs.azure.cn/en-us/cosmos-db/index-policy
NEW QUESTION # 39
An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?
Answer: B
Explanation:
Creating a B-tree index on the department column will help, but only if you use the correct indexing strategy, as a standard B-tree index alone is often ignored during vector searches.
When you run a query that filters by metadata before performing a vector similarity search, PostgreSQL must choose between filtering the rows first or searching the vector index first. In many cases, a standard B-tree index on the metadata column combined with an HNSW/IVFFlat index on the vector column results in pre-filtering that defaults to a sequential scan, or post- filtering that returns fewer results than requested.
Reference:
https://learn.microsoft.com/en-us/azure/horizondb/ai/vector-search-pgvector
NEW QUESTION # 40
Hotspot Question
You are using Python SDK to develop a containerized AI application that retrieves the value of a runtime setting stored in an Azure App Configuration resource. The application will run in Azure in the security context of a managed identity.
The application must work in a local development environment without any code changes.
You need to complete the code that implements the retrieval.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: DefaultAzureCredential
This class from the azure-identity library provides an automated, sequenced token authentication flow. When running locally, it automatically falls back to your local development environment credentials (such as Azure CLI, environment variables, or VS Code login). Once deployed to Azure, it seamlessly switches to the environment's managed identity without requiring any code changes.
Box 2: get_configuration_setting
This method belongs to the AppConfigurationClient in the azure-appconfiguration SDK. It is used to fetch a specific configuration key-value pair using its key name ("FeatureX") and an optional label.
Reference:
https://learn.microsoft.com/en-us/azure/developer/python/tutorial-containerize-deploy-python-web-app-azure-04
NEW QUESTION # 41
Drag and Drop Question
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
- Reference the Azure Key Vault without requiring any changes to the
Java code.
- Dynamically add and remove instances of the Azure Functions host
based on the number of incoming application events.
- Ensure that instances are perpetually warm to avoid any cold starts.
- Connect to a VNet.
- Authentication to the Azure Key Vault instance must be removed if the Azure Functions application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
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:
Explanation:
Step 1: Create the Azure Functions app with a Premium plan type
Perpetually Warm & VNet: The Premium plan type is the only serverless-scale hosting option that satisfies requirement 3 (keeping instances warm to eliminate cold starts) and requirement 4 (native regional VNet integration). The Consumption plan type does not keep instances warm and does not support VNet injection.
Event-Driven Scaling: It fulfills requirement 2 by dynamically adding and removing host instances using the event-driven scale controller.
Step 2: Create a system-assigned managed identity for the application
Lifecycle Coupling: It directly satisfies requirement 5. A system-assigned managed identity is tied strictly to the lifecycle of the Azure Functions resource. If the Functions application is deleted, Azure automatically removes the identity and its authentication token, ensuring that access to the Key Vault is immediately revoked. A user-assigned identity exists as an independent resource and would persist after the application's deletion.
Step 3: Create an access policy in Azure Key Vault for the application identity Granting Permissions: This step completes the access requirements. By adding an access policy (or an Azure RBAC role assignment) in Azure Key Vault specifically for the created system- assigned managed identity, you grant the runtime environment the necessary permissions to retrieve secrets.
No Code Changes: Combining the managed identity with an App Setting configured as an Azure Key Vault Reference allows the Functions platform to fetch secrets automatically at runtime, keeping your Java source code unchanged Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-best-practices
NEW QUESTION # 42
Hotspot Question
A Python web API uses OpenTelemetry for tracing. The call_downstream_service function makes an outbound HTTP request by using the requests library.
The following code is the only OpenTelemetry configuration in the application:
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:
Box 1: Yes
This code will successfully create a local span for the process_request function, but it will not automatically create or propagate spans for the outbound HTTP request made by the requests library.
Box 2: No
Manual tracing only: Your current configuration only creates a span for the code wrapped inside the with tracer.start_as_current_span(...) context manager.
Box 3: Yes
Missing instrumentation: The standard requests library does not natively know about OpenTelemetry. It requires an external instrumentation package to hook into its network calls.
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/core-tracing-opentelemetry-readme
NEW QUESTION # 43
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