AI-900 Top Questions, New AI-900 Exam Practice

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Describe features of Generative AI workloads on Azure15-20%- Identify responsible AI considerations for generative AI
- Describe generative AI concepts
- Describe Azure OpenAI Service capabilities
Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads
Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify common NLP tasks
- Describe Azure capabilities for NLP
- Identify Azure AI services for NLP
Describe fundamental principles of machine learning on Azure30-35%- Describe Azure Machine Learning capabilities
- Describe core machine learning concepts
- Identify common machine learning tasks
- Describe features of no-code automated ML
Describe features of computer vision workloads on Azure15-20%- Describe Azure capabilities for computer vision
- Identify common computer vision tasks
- Identify Azure AI services for computer vision

>> AI-900 Top Questions <<

100% Pass Quiz Updated AI-900 - Microsoft Azure AI Fundamentals Top Questions

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Microsoft Azure AI Fundamentals Sample Questions (Q79-Q84):

NEW QUESTION # 79
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Ingest and prepare a dataset.
2 - Split the data randomly into training data and validation data
3 - Train the model.
4 - Evaluation the model against the validation dataset.


NEW QUESTION # 80
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 81
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 82
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script. You need to generate a narration audio file for each video based on the script. Which type of workload should you use?

Answer: D

Explanation:
Speech synthesis, also known as text-to-speech (TTS), is the AI workload that converts written text into spoken words. In this case, the task is to generate narration audio from provided scripts for silent instructional videos.
Speech recognition performs the opposite function - it converts speech into text. Language modeling is for text understanding and prediction (e.g., GPT). Translation converts text between languages, not from text to audio.
Therefore, the most appropriate workload, according to Microsoft's AI-900 study material under the "Speech AI capabilities" section, is speech synthesis, which enables natural voice narration generation.


NEW QUESTION # 83
You are building an Al-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?

Answer: C

Explanation:
According to Microsoft's Responsible AI Principles, transparency refers to ensuring that AI systems are understandable and that their decisions or predictions can be explained clearly to users. When building an AI- based loan approval app, documenting the reasons for approving or rejecting a loan and making this information available to the applicant ensures that the system's decision-making process is transparent and easily interpretable.
Transparency in AI involves making the model's inputs, features, and reasoning process visible and comprehensible to both developers and users. For a loan approval application, this could mean showing which factors-such as income, credit score, or debt ratio-influenced the outcome. Microsoft emphasizes that users should be aware when AI is making decisions that affect them and should have access to an explanation of how those decisions were made.
* Fairness (A) ensures that AI systems treat all individuals equitably without bias.
* Inclusiveness (B) focuses on accessibility and ensuring AI benefits all groups.
* Accountability (D) ensures that humans remain responsible for the outcomes of AI systems.
While accountability supports ethical oversight, the specific act of explaining and documenting the AI's decision process aligns directly with transparency.
Therefore, the verified answer is C. Transparency.


NEW QUESTION # 84
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