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Microsoft AI-901 Exam Syllabus Topics:

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

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

NEW QUESTION # 79
Match the principles of responsible Al to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the light. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation:

AI systems must consistently operate as intended, even under unexpected conditions. = Reliability and safety This description matches the Microsoft responsible AI principle of reliability and safety , which focuses on ensuring that AI systems behave consistently, safely, and as expected, including under unusual or unexpected conditions.
AI systems must protect and secure personal and business information. = Privacy and security This description matches the Microsoft responsible AI principle of privacy and security , which focuses on protecting personal, confidential, and business-sensitive information and preventing unauthorized access or misuse.

NEW QUESTION # 80
Drag and Drop Question
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Fairness
Fairness (or non-discrimination/equity) is the responsible AI principle that applies when a system must not discriminate based on gender, race, or age. This principle ensures the AI treats all people fairly and does not create or copy unfair bias.
Box 2: Privacy and security
Privacy and security is the responsible AI principle that best fits when personal data must be visible only to approved users.
Data Protection: It requires safeguarding user data from unauthorized access.
Access Control: It ensures only authenticated and authorized users view sensitive information.
Trust Safeguards: It prevents data leaks and maintains user confidentiality.
Box 3: Transparency
The Transparency responsible AI principle requires that artificial intelligence systems and their operations are open, understandable, and properly disclosed so that users and stakeholders know when they are interacting with AI, how decisions are made, and what the system's limitations are.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2

NEW QUESTION # 81
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.

Which type of natural language processing was performed?

A. translation
B. sentiment analysis
C. key phrase extraction
D. entity recognition

Answer: D

Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition , also known as Named Entity Recognition (NER) . NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.

NEW QUESTION # 82
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 # 83
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. Adjust the threshold value.
B. Increase the number of training iterations.
C. Increase the amount of training data.
D. Use a different Machine Learning model.

Answer: A

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 # 84
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