Latest AI-200 Questions, Latest AI-200 Exam Topics

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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Topic 1: Secure, monitor, troubleshoot Azure solutions- Operate AI cloud solutions
  • 1. Troubleshooting Azure solutions
  • 2. Performance optimization
  • 3. Monitoring and observability
  • 4. Security and secret management
Topic 2: Develop AI solutions by using Azure data management services- Work with Azure data platforms for AI workloads
  • 1. Data integration for AI applications
  • 2. Azure data management services
  • 3. Vector databases
Topic 3: Connect to and consume Azure services- Integrate Azure services
  • 1. Azure messaging and eventing
  • 2. Event-driven architectures
  • 3. Azure SDKs
  • 4. Serverless integration patterns
  • 5. Third-party SDKs
Topic 4: Develop containerized solutions on Azure- Implement containerized applications
  • 1. Manage containerized compute environments
  • 2. Deploy AI workloads in containers
  • 3. Implement scalable hosting patterns

>> Latest AI-200 Questions <<

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q97-Q102):

NEW QUESTION # 97
You are training a Language Understanding model for a user support system.
You create the first intent named GetContactDetails and add 200 examples.
You need to decrease the likelihood of a false positive.
What should you do?

Answer: C

Explanation:
To reduce false-positive intent predictions , add representative utterances to the None intent . The None intent exists specifically for user utterances that should not map to any defined business intent. Microsoft recommends adding examples that resemble potential false positives so that the model learns a stronger decision boundary between valid intent utterances and unrelated or ambiguous input.
For example, if the GetContactDetails intent contains requests such as "give me the customer phone number," the None intent should contain similar-looking but semantically unrelated phrases that could otherwise be incorrectly classified as GetContactDetails. During prediction, an utterance can be classified as None when it resembles None-training examples or when the highest intent score falls below the configured None threshold. Microsoft specifically advises adding false-positive examples to the None intent to improve intent discrimination.
Adding more examples to GetContactDetails alone generally strengthens recognition of that intent but does not provide the model with enough negative examples. A machine-learned entity addresses entity extraction rather than intent classification. Active learning helps identify uncertain utterances for review, but it is not the direct corrective action requested.
Study Guide references: Azure AI Language # Conversational Language Understanding; intents; None intent; intent classification; reducing false positives.


NEW QUESTION # 98
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule.
You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: B,D

Explanation:
[A]
QeueName: The exact name of the specific Azure Storage queue your app is polling is required.
[C]
Why Trigger Type is Required
When configuring a custom scale rule in Azure Container Apps (ACA), the platform utilizes KEDA underneath. Because a custom rule can connect to many different event sources (such as Kafka, Redis, or Azure Storage), you must explicitly define the Trigger type (e.g., azure-queue) so KEDA knows which specific scaler to initiate.
Reference:
https://techcommunity.microsoft.com/blog/fasttrackforazureblog/container-apps-a-practical-scaling-with-azure-queue-scale-rule/3722075


NEW QUESTION # 99
You must ensure that PII (personally identifiable information) such as names and phone numbers is automatically redacted from support ticket transcripts before they are stored. What should you use?

Answer: D

Explanation:
Azure AI Language's PII detection feature identifies and can redact categories of sensitive information (names, phone numbers, SSNs, etc.) from unstructured text, which is the purpose- built service for this scenario.


NEW QUESTION # 100
You deploy multiple instances of a change feed processor to handle a high ingestion rate within Azure Cosmos DB for NoSQL.
Each processor instance must process a different subset of partitions.
You need to ensure the workload is load-balanced across all processor instances.
What should you configure?

Answer: C


NEW QUESTION # 101
Your chat application calls Azure OpenAI Service. You need to prevent the model from returning content that promotes self-harm, even if the prompt is crafted to bypass instructions. What should you configure?

Answer: D

Explanation:
System messages can be circumvented by adversarial prompting. Content Safety filters operate independently of the model and inspect both input and output, blocking harmful categories (including self-harm) regardless of prompt engineering.


NEW QUESTION # 102
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