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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 47
An AI application retrieves configuration values from App Configuration. The application uses static configuration.
You need to implement a solution that supports dynamic configuration updates and minimizes latency for application requests.
What should you do?
Answer: B
Explanation:
Use the Azure App Configuration provider ' s caching and dynamic refresh mechanism with a configured refresh interval . The application loads configuration into a local cache and periodically checks App Configuration for changes. When a registered key changes, the provider refreshes the cached configuration without requiring an application restart. Microsoft documents SetRefreshInterval and equivalent provider settings as the mechanism for controlling the minimum time between configuration-refresh checks.
This approach satisfies both requirements. The configuration is dynamic because updated values can be detected and reloaded at runtime, while request latency remains low because normal application requests read configuration from the local cached copy instead of making a remote App Configuration request every time.
For request-driven refresh, Microsoft notes that refresh occurs asynchronously and does not block the incoming request. Before the configured refresh interval expires, additional refresh calls effectively perform no remote operation.
Microsoft also recommends caching App Configuration values instead of loading them whenever they are used, both to reduce latency and to avoid excessive service requests, throttling, and unnecessary charges.
Environment variables remain static unless the application is restarted or redeployed. Retrieving configuration on every request increases latency and service load. Key Vault is primarily intended for secrets, not general dynamic application configuration.
Study Guide references: Azure App Configuration; dynamic configuration refresh; provider caching; refresh intervals; request-driven refresh; configuration resiliency.
NEW QUESTION # 48
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Verified Answer: 1) Enable ingress; 2) add the required DNS records; 3) validate ownership of the custom domain; 4) add the custom domain to the Container App; 5) bind the certificate.
Detailed Explanation: A custom HTTPS hostname requires an ingress endpoint, DNS proof that the requester controls the hostname, the hostname association itself, and a TLS certificate bound to that hostname.
DNS records must exist before ownership validation can succeed. Once ownership is validated, the domain can be associated with the Container App and the certificate can be bound to provide HTTPS. Skipping ingress or DNS validation would prevent the hostname from being correctly routed and secured.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Custom domains and certificates in Container Apps
NEW QUESTION # 49
You deploy a private container image from Azure Container Registry (ACR) to App Service.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
uthenticate App Service to ACR: assign the registry pull role to a managed identity. Avoid static credentials:
configure App Service to use that managed identity for the image pull.
Detailed Explanation: App Service can pull a private image from Azure Container Registry by using a managed identity rather than registry username/password credentials. The identity must be granted an appropriate pull-only registry role at the registry scope, preserving least privilege. A webhook does not authenticate an image pull, and enabling the ACR admin account or storing a registry password would introduce static credentials. The managed-identity approach satisfies both secure authentication and credential- elimination requirements.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Configure a custom container for App Service | Managed identities for Azure resources
NEW QUESTION # 50
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification.
What should you implement?
Answer: B
Explanation:
A retrieval-augmented generation (RAG) architecture is the correct mechanism when generated answers must be traceable to specific source documents. RAG first retrieves relevant document chunks from a search index or other knowledge source, supplies those chunks to the language model as grounding context, and then generates an answer based on that retrieved information. Microsoft specifically documents that RAG enables responses containing citations back to source content .
For reliable citation generation, each retrieved chunk should carry identifying metadata such as a source identifier, document title, section, page, or URL . Microsoft's Azure RAG guidance recommends labeling retrieved chunks and including source metadata with them, then explicitly instructing the model to cite the corresponding sources in its response. This allows users to verify individual claims against the underlying documents.
Modern Azure AI Search agentic retrieval can also return structured references and grounding metadata , specifically designed for citation linking.
Increasing top_p only alters token sampling. Content filtering addresses safety, not provenance. Using a base model without retrieval provides no authoritative document linkage.
Study Guide references: RAG architecture; grounding data; retrieval metadata; Azure AI Search references; prompt engineering for citations.
NEW QUESTION # 51
You are investigating high latency in an AI search application that processes millions of requests daily.
Telemetry is stored in Azure Monitor Logs.
You must create a KQL query that correlates information from the AppRequests table and the AppDependencies table. The query must meet the following requirements:
* Include only data from the last 24 hours.
* Filter for failed requests only.
* Calculate the average duration of dependencies, grouped by operation.
The query must follow best practice to optimize the performance by minimizing the initial data scan.
You need to create the query.
In which order should perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Verified answer: 1) Select the requests table; 2) apply the 24-hour time filter; 3) filter failed requests; 4) join the dependencies table; 5) summarize average dependency duration by operation.
Detailed Explanation: KQL performs best when high-selectivity filters are applied as early as possible, especially the time predicate that limits the amount of data scanned. Starting from AppRequests, restricting to the last 24 hours, and then filtering failures reduces the left-side dataset before the join. The dependency table is then correlated with those requests, and aggregation is performed last to calculate average dependency duration by operation. Joining or summarizing before the time and failure filters would process more data than necessary.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Optimize log queries in Azure Monitor
NEW QUESTION # 52
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