AI-901 Exam Dumps : Microsoft Azure AI Fundamentals (Updated Version)

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.

To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.

PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.

The other options are incorrect:

A. custom text classification categorizes documents or text into labels.

C. entity linking identifies entities and links them to knowledge base entries.

D. custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.

Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes

When a deployed generative AI model receives a user prompt and generates an output, that process is called inference . The model is being used to produce a response, not being trained.

Statement 2: A generative AI model generates responses by copying stored documents directly from the model’s training data. = No

Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.

Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model’s training data. = Yes

This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.