AI-102 Exam Dumps : Designing and Implementing a Microsoft Azure AI Solution

Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an “entity for price” would not make the chatbot correctly answer “How much does cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).

CLU entities vs. QnA authoring—separate features and workflows.

When deploying Cognitive Services containers (such as Anomaly Detector), the container requires a billing parameter in the docker run command.

This parameter points to the Azure resource key and endpoint for billing.

Fluentd is for logging.

Http Proxy is optional for outbound connectivity.

Mounts are used for local storage but are not mandatory for billing.

Correct Answer: B

To quickly prototype harmful-content detection (e.g., discriminatory language) with minimal development, you should provision an Azure AI Content Safety resource in the Azure portal and then try it in Content Safety Studio using “Moderate text content.” Content Safety Studio lets you test text moderation, tune thresholds, and export sample code without writing much code up front. Creating an Azure OpenAI resource or using “Protected material detection for text” isn’t required for abusive/hate moderation; “protected material” targets copyrighted/credential leakage scenarios, not general toxicity.

Microsoft References:

Quickstart: Analyze text content (use Moderate text content in the studio; prerequisite is to create a Content Safety resource). Microsoft Learn

Azure AI Content Safety overview