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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement AI solutions by using Microsoft Foundry55-60%- Implement AI solutions
  • 1. Integrate AI services into applications
  • 2. Implement single-agent solutions
  • 3. Use code examples to call AI services
  • 4. Monitor and manage AI solutions
- Work with models in Microsoft Foundry
  • 1. Discover and deploy foundation models
  • 2. Fine-tune models
  • 3. Customize models with prompt engineering
  • 4. Evaluate model performance and safety
- Get started with Microsoft Foundry
  • 1. Describe core features and use cases of Microsoft Foundry
  • 2. Create and configure a Foundry project
  • 3. Navigate the Microsoft Foundry portal
Topic 2: Identify AI concepts and responsibilities40-45%- Identify AI concepts and features
  • 1. Describe characteristics of generative AI
  • 2. Describe capabilities of machine learning
  • 3. Describe capabilities of computer vision
  • 4. Describe types of AI workloads
  • 5. Describe capabilities of natural language processing
- Describe principles of responsible AI
  • 1. Describe accountability considerations
  • 2. Describe privacy and security considerations
  • 3. Describe inclusiveness considerations
  • 4. Describe fairness considerations
  • 5. Describe reliability and safety considerations
  • 6. Describe transparency considerations
- Identify AI model components and configurations
  • 1. Describe model training and evaluation concepts
  • 2. Describe model deployment and consumption concepts
  • 3. Describe data preparation for AI models

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Microsoft Azure AI Fundamentals Sample Questions (Q65-Q70):

NEW QUESTION # 65
For each of the following statements, select Yes if the statement is true, Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Statement
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes . Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes . A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No . Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.


NEW QUESTION # 66
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Statement 1: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models, not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.


NEW QUESTION # 67
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Counting the number of animals in an area based on a video feed is an example of computer vision.
Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.
Forecasting predicts future values or trends from historical data.
Knowledge mining extracts insights from large document or data collections.
Therefore, the correct answer is computer vision.


NEW QUESTION # 68
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 69
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 70
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