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| Section | Weight | Objectives |
|---|---|---|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
>> AI-200 Exam Objectives Pdf <<
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NEW QUESTION # 20
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Support percentage-based, targeted rollouts: Feature flag
* Maximize token limits per request: Key-value
* Store a key that is retrieved securely at runtime: Key Vault reference
* Reference a list of supported deployment regions: Key-value
Azure App Configuration provides three distinct mechanisms for these scenarios. A Feature flag is the correct choice for percentage-based and targeted rollouts. Feature Management supports conditional activation based on filters such as percentage allocation, users, groups, and other targeting criteria, making it appropriate for gradual deployment and controlled experimentation.
A Key-value is the correct mechanism for ordinary runtime configuration data such as a model token limit or a list of supported deployment regions. These values are application settings rather than secrets or feature- state controls. The application can retrieve and refresh them dynamically without redeployment. A token-limit setting such as MaxTokensPerRequest and a region list such as SupportedRegions fit naturally into App Configuration key-value entries.
For sensitive data, use a Key Vault reference . App Configuration stores only the reference to the Azure Key Vault secret rather than the secret value itself. At runtime, the application resolves the secret securely, typically by using a managed identity. This avoids embedding credentials or sensitive keys in configuration values.
Therefore, the correct mapping is Feature flag # Key-value # Key Vault reference # Key-value .
Study Guide references: Azure App Configuration # Feature Management; key-values; Key Vault references; dynamic application configuration.
NEW QUESTION # 21
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 # 22
You are developing an AI search API that caches semantic search results in Redis.
Search results must remain cached for 10 minutes. If the underlying data changes, cached entries must NOT be returned.
You need to implement a cache-aside strategy to ensure data consistency.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Delete keys on mutation. Implement an event-driven or database-hook system to purge related Redis keys the moment the source data updates.
Use absolute expiration. Set a strict, un-extendable Time-To-Live (TTL) of 10 minutes when writing to the cache, ensuring the data naturally expires even if a deletion event is missed.
Incorrect:
[Not C]
Implementing a sliding expiration is not a good step for this specific architecture because it violates the requirement that entries must not be returned if the underlying data changes.
Reference:
https://medium.com/real-world-net/net-caching-strategies-compared-redis-vs-memory-vs-hybrid-b8b8be1e708d
NEW QUESTION # 23
You are designing an Azure Function app that exposes a public API.
The solution must:
- Validate incoming request data and return results immediately to the
caller.
- Support Microsoft Entra ID authentication.
- Guarantee idempotent processing when the same request is retried.
- Scale automatically under variable load.
- Avoid duplicate processing.
You need to implement a trigger.
Which trigger should you implement?
Answer: A
Explanation:
An HTTP Trigger combined with Azure Durable Functions (Asynchronous Http Start pattern) and a backing database is the most appropriate architecture to fulfill all your requirements.
Immediate validation: HTTP triggers allow synchronous request-response communication, enabling you to validate payload data and return a 200 OK or 400 Bad Request to the caller instantly.
Entra ID authentication: Azure Functions native App Service Authentication (Easy Auth) integrates directly with HTTP endpoints to enforce Microsoft Entra ID token validation before your code even runs.
Automatic scaling: HTTP-triggered functions scale dynamically out-of-the-box using the Azure Functions Consumption or Premium hosting plans based on incoming web traffic.
Reference:
https://github.com/microsoft/woodgrove-auth-api/blob/main/README.md
NEW QUESTION # 24
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 25
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