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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Identify common machine learning tasks - Describe core machine learning concepts - Describe Azure Machine Learning capabilities |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 5: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
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NEW QUESTION # 309
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:
NEW QUESTION # 310
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data.
In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories.
The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:
* Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).
* Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).
* Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation).
Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction.
Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.
NEW QUESTION # 311
Which statement is an example of a Microsoft responsible AI principle?
Answer: D
Explanation:
The correct answer is C. AI systems must be understandable, which corresponds to the Transparency principle of Microsoft's Responsible AI framework.
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI", Microsoft defines six key principles for responsible AI:
* Fairness - AI systems should treat everyone equitably.
* Reliability and safety - AI should function as intended, even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower everyone and engage diverse users.
* Transparency - AI systems should be understandable.
* Accountability - People should be accountable for AI systems.
The statement "AI systems must be understandable" aligns directly with the Transparency principle, ensuring that AI decisions and behaviors can be explained and interpreted by developers, users, and stakeholders.
Microsoft emphasizes that transparent AI builds trust, allows debugging, and ensures ethical usage.
Other options are incorrect:
* A. Use only publicly available data - Not a principle of Responsible AI.
* B. Protect the interests of the company - Focused on business goals, not ethical AI.
* D. Keep personal details public - Violates the Privacy and Security principle.
# Final Answer (Q179): C. AI systems must be understandable.
NEW QUESTION # 312
Which scenario is an example of a webchat bot?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, a webchat bot is defined as a conversational AI application that interacts with users through a web-based chat interface. It simulates human conversation using text (and sometimes voice) to answer questions, assist with transactions, or provide information automatically. Microsoft Learn's "Describe features of common AI workloads" module highlights conversational AI as a major AI workload, where bots and virtual agents are used to provide automated, intelligent responses in real time through web, mobile, or messaging platforms.
In this scenario, the chatbot on the festival website provides immediate answers about scheduled events and ticket purchases. This aligns exactly with how a webchat bot operates - interacting with users through a website, handling repetitive inquiries, and providing consistent information without human intervention. This type of solution is commonly built using Azure Bot Service integrated with Azure Cognitive Services for Language, which allows the bot to understand user intent and respond naturally.
Let's examine the other options to reinforce why D is correct:
* A describes a text analytics or sentiment analysis scenario, not a conversational bot, because it classifies text sentiment but doesn't "chat" with a user.
* B is an example of machine translation using the Translator service, not a chatbot.
* C is an email classification or natural language processing task, not a webchat interaction.
The AI-900 exam objectives clearly distinguish conversational AI from other cognitive services such as translation or sentiment analysis. Conversational AI focuses on dialogue and interaction through natural language conversation channels like websites or messaging apps.
Therefore, the verified and officially aligned answer is D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
NEW QUESTION # 313
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 # 314
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