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| Section | Weight | Objectives |
|---|---|---|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
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They work together and strive hard to design and maintain the top standard of Microsoft AI-200 exam questions. So you rest assured that with the Microsoft AI-200 exam questions you will not only ace your Microsoft AI-200 certification exam preparation but also be ready to perform well in the final Microsoft Developing AI Cloud Solutions on Azure exam. The AI-200 Exam are the real AI-200 exam practice questions that will surely repeat in the upcoming Microsoft AI-200 exam and you can easily pass the exam.
NEW QUESTION # 31
Hotspot Question
You are troubleshooting latency issues by using the requests table in Azure Monitor.
You need to calculate the average request duration grouped by cloud role name and also limit results to the past 30 minutes.
Which code elements should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: where
In Kusto Query Language (KQL), the where operator filters the rows of a table to only include records that satisfy a specific predicate or condition.
Box 2: summarize
summarize is used for aggregations (count, avg, percentiles).
Reference:
https://world.optimizely.com/blogs/K-Khan-/Dates/2025/12/troubleshooting-with-azure-application-insights-using-kql
NEW QUESTION # 32
You must ensure an Azure OpenAI-powered application never exceeds a defined token-per-minute budget across all users to avoid runaway costs.
What should you configure?
Answer: A
Explanation:
Configure deployment-level Tokens-per-Minute (TPM) and Requests-per-Minute (RPM) rate limits for the Azure OpenAI model deployment. Azure OpenAI quota is allocated to individual model deployments in TPM units, and Microsoft documents that the TPM assigned to a deployment directly maps to the rate limit enforced for inference requests against that deployment. An associated RPM limit is also enforced according to the model ' s TPM-to-RPM ratio.
Because all application users invoke the same deployment, this enforcement occurs at the deployment boundary rather than independently for each end user. Once request traffic would exceed the configured capacity, Azure applies throttling rather than allowing unrestricted token consumption. Microsoft Foundry additionally supports explicit token-management controls through AI Gateway, where a TPM limit can be set for a model deployment; requests exceeding the limit receive HTTP 429 Too Many Requests responses.
Content filtering governs safety categories, not consumption. An Azure AI Search vector-index limit controls search storage rather than generated tokens. Temperature changes sampling randomness and has no enforcement role in rate or cost control.
Study Guide references: Azure OpenAI quota management; TPM/RPM rate limits; deployment capacity; throttling; Foundry AI Gateway token management.
NEW QUESTION # 33
You publish custom AI inference events to an Event Grid topic.
Downstream services must receive events only if the confidenceScore value is greater than 0.80.
You need to filter events based on a numeric value in the event payload.
Which filter should you configure?
Answer: B
Explanation:
An Advanced Filter using the NumberGreaterThan operator configured with a key path pointing to the payload value (e.g., data.confidenceScore) and a comparison value of 0.80 should be set up.
To filter custom payload data based on a numeric threshold in Azure Event Grid, configure the event subscription with these properties:
Key: data.confidenceScore (adjust the path depending on your exact JSON payload structure) Operator: NumberGreaterThanValue: 0.80 Reference:
https://learn.microsoft.com/en-us/azure/event-grid/event-filtering
NEW QUESTION # 34
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by using a parameterized query. You must minimize the possibility of SQL injection.
How should you update the implementation to execute the SQL statement safely? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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:
Verified answer: Modify SQL: use a parameterized query. Bind input: pass the ID as an argument. Execute:
supply the parameter tuple to the SDK/driver method.
Detailed Explanation: The security boundary is created by separating SQL syntax from user-supplied values.
The statement should contain a parameter placeholder rather than concatenated input, and the document ID should be supplied separately through the database driver's parameter mechanism. The driver then performs the correct protocol-level binding and escaping. Manually escaping quotation marks is error-prone and does not provide the same injection resistance as true parameterization.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector similarity search with Azure PostgreSQL
NEW QUESTION # 35
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: C
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 # 36
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