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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.
Topic 2
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.

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Microsoft Azure AI Fundamentals Sample Questions (Q128-Q133):

NEW QUESTION # 128
You have a Microsoft Foundry project that contains a vision-enabled model deployment. You need to create a prompt that ensures the model produces a relevant and useful response. What should you include in the prompt?

Answer: A

Explanation:
To ensure a vision-enabled model produces a relevant and useful response, the prompt should include a clear description of the task . For example, you should tell the model what to do with the image, such as describe it, identify objects, compare images, extract visible text, or answer a specific question about the visual content.
The other options are incorrect:
B). a dataset ID for the image is not needed in the prompt.
C). the version number of the model is deployment/configuration information, not prompt content.
D). the deployment name of the model is used in the API call to route the request, not inside the prompt.


NEW QUESTION # 129
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.


NEW QUESTION # 130
Stating the source of the data used to train a model is an example of which responsible Al principle?

Answer: A

Explanation:
The correct answer is A. transparency . In Microsoft's Responsible AI framework, transparency requires that people can understand relevant aspects of how an AI system was created, how it operates, what information it relies upon, and why it produces particular outcomes. The current Microsoft Azure AI Fundamentals study guide specifically includes "Describe considerations for transparency in an AI solution" under the responsible AI objectives.
Identifying and documenting the source of training data directly supports transparency because it establishes data provenance. Stakeholders can determine where the model's knowledge originated, assess the appropriateness and limitations of the dataset, investigate potential biases, and better understand factors that may influence model behaviour. Microsoft also describes transparency as helping stakeholders understand how and why AI systems work , with interpretability and information about datasets and model characteristics contributing to trust and auditability.
Privacy and security concerns protecting data and controlling its use. Reliability and safety concerns predictable, robust operation. Fairness focuses on avoiding systematically unequal outcomes across groups.
Merely disclosing where training data came from is therefore principally a transparency practice.
Study Guide Reference: Describe principles of responsible AI # Describe considerations for transparency in an AI solution.


NEW QUESTION # 131
In Azure Machine Learning, what are two ensemble methods for combining models in automated machine learning (automated ML)? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: A,B

Explanation:
In Azure Machine Learning's automated machine learning (AutoML), two prominent ensemble methods for combining models are Voting and Stacking.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml 1


NEW QUESTION # 132
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?

Answer: B

Explanation:
A text generation model is the correct tool when you want an AI solution that responds to user queries with detailed written explanations.
Natural Language: They write clear sentences and paragraphs.
Complex Ideas: They break down hard topics into simple words.
Flexible Format: They can make lists, summaries, or long guides.


NEW QUESTION # 133
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