AI-901 Exam Prep - AI-901 Reliable Exam Sample

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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Describe fundamental principles of machine learning on Azure	25-30%	- Machine learning concepts 1. Training and evaluation concepts 2. Regression and classification 3. Supervised vs unsupervised learning - Azure Machine Learning basics 1. AutoML concepts 2. Azure ML workspace components
Describe features of natural language processing (NLP) workloads on Azure	15-20%	- Azure NLP services 1. Azure AI Language 2. Speech services (speech-to-text, text-to-speech) - Text analysis 1. Key phrase extraction 2. Language detection 3. Sentiment analysis
Describe features of generative AI workloads on Azure	10-15%	- Azure OpenAI and generative services 1. Azure OpenAI Service capabilities 2. Use cases: chatbots, content generation, summarization - Generative AI concepts 1. Responsible use of generative AI 2. Large language models (LLMs) 3. Prompt engineering basics
Describe features of computer vision workloads on Azure	15-20%	- Azure AI Vision services 1. Face detection and analysis (limited use cases) 2. Azure AI Vision - Image analysis 1. Image classification 2. Optical character recognition (OCR) 3. Object detection
Describe Artificial Intelligence workloads and considerations	20-25%	- Responsible AI principles 1. Privacy and security 2. Reliability and safety 3. Transparency and accountability 4. Fairness 5. Inclusiveness - Identify features of common AI workloads 1. Natural language processing use cases 2. Document processing and extraction 3. Computer vision use cases 4. Conversational AI and chatbots

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Microsoft Azure AI Fundamentals Sample Questions (Q27-Q32):

NEW QUESTION # 27
You have an Azure Machine Learning model that generates a large quantity of false negatives.
You need to reduce the number of false negatives without re-training the model.
What should you do?

A. Increase the amount of training data.
B. Increase the number of training iterations.
C. Use a different Machine Learning model.
D. Adjust the threshold value.

Answer: D

Explanation:
What are different methods to minimize False Negatives and False Positives in Binary Classification?
Threshold Line
One of the easiest methods to minimize the outcomes of a certain case is simply changing the decision boundary line from the basic 0.5 to above (when reducing False Positives) or below (when reducing False Negatives). It should be noted that by doing this, the possibility of False Positives increases. In other words, by decreasing the False Negatives we are increasing the False Positives.
Reference:
https://www.kaggle.com/discussions/general/376229

NEW QUESTION # 28
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: perform consistently under expected conditions
Reliability and safety is the specific Microsoft Responsible AI principle defined by the requirement that AI systems must operate reliably, safely, and consistently under expected conditions.
Reference:
https://learn.microsoft.com/en-us/agents/design-guidelines/responsible-ai

NEW QUESTION # 29
Drag and Drop Question
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini- gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 30
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:

NEW QUESTION # 31
Hotspot Question
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:

NEW QUESTION # 32
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