Important Microsoft AI-901 Exam Questions

Answer: B

Explanation:

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.

Why the other options are incorrect:

A . Provide explanations about how predictions are generated = Transparency C . Evaluate model outputs across demographic groups to reduce bias = Fairness D . Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

Answer: B

Explanation:

The correct text analysis technique is Named Entity Recognition (NER).

Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations.

Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.

Answer: B

Explanation:

To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.

Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

Why the other options are incorrect:

A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs. C . Compliance relates to governance and compliance, not model cost comparison. D . Tools provides Foundry tools, not benchmarked cost comparison across models.

Answer: B

Explanation:

To minimize cost and keep generated responses within a defined length, configure Max Completion Tokens.

Microsoft's Azure OpenAI / Foundry API reference defines max_completion_tokens as an upper bound for the number of tokens that can be generated for a completion. Because generated tokens contribute to usage and response length, limiting completion tokens helps control both output length and cost.

Temperature and Top P control randomness or sampling behavior, not maximum response length. Model version settings do not directly define the generated response length.

Answer: C