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| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Identify types of NLP solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning |
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NEW QUESTION # 177
You are designing a system that will generate insurance quotes automatically.
Match the Microsoft responsible Al principles to the appropriate requirements.
To answer, drag the appropriate principle 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.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Microsoft's Responsible AI principles are the foundation for developing and deploying ethical and trustworthy AI systems. The six key principles are Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle guides specific practices for ensuring AI systems operate responsibly in real-world applications like automated insurance quoting systems.
* Transparency - This principle ensures that the AI's decisions can be understood and explained.
Recording the decision-making process and enabling staff to trace how a quote was generated aligns with transparency. It allows stakeholders to interpret the reasoning behind model outputs, ensuring that the AI behaves predictably and ethically.
* Privacy and Security - This principle focuses on protecting personal data and ensuring that sensitive information is handled responsibly. Limiting access to customer data only to authorized personnel maintains compliance with privacy laws (like GDPR) and safeguards against misuse. Microsoft emphasizes that AI systems should maintain strict control over data visibility and integrity.
* Inclusiveness - This principle ensures that AI systems are accessible to all users, including people with disabilities. By supporting screen readers and assistive technologies, the system ensures equal access to information and services for every customer. Inclusiveness prevents discrimination and promotes accessibility, both of which are central to Microsoft's Responsible AI strategy.
Thus, the correct mapping of principles is:
* Decision process # Transparency
* Personal information visibility # Privacy and Security
* Accessibility via screen readers # Inclusiveness.
NEW QUESTION # 178
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
NEW QUESTION # 179
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
Feature engineering may include tasks such as:
* Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price × quantity).
* Extracting time-based components (e.g., year, month, day, hour) from datetime values.
* Encoding categorical variables (e.g., one-hot encoding or label encoding).
* Scaling or normalizing numerical features.
* Creating polynomial or interaction terms to capture complex relationships.
Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
The other options do not fit this definition:
* Feature selection is about removing redundant or irrelevant features, not generating new ones.
* Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
* Model training is the phase where the algorithm learns patterns from the data, not when features are created.
Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.
NEW QUESTION # 180
You have a webchat bot that provides responses from a QnA Maker knowledge base.
You need to ensure that the bot uses user feedback to improve the relevance of the responses over time.
What should you use?
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and the official Microsoft Learn module "Describe features of common AI workloads", QnA Maker (now part of Azure AI Language services) allows developers to build, train, and publish a knowledge base that provides natural-language answers to user queries. A key capability of this service is active learning, which enables the knowledge base to automatically suggest improvements by analyzing user feedback and usage patterns.
Active learning is an iterative process in which the service observes real user interactions and identifies ambiguous questions or pairs of similar questions that produce uncertain or multiple answers. The system then recommends updates or refinements to the knowledge base to improve the accuracy and relevance of responses. This feedback loop helps ensure that over time, the chatbot's responses align more closely with actual user expectations and language variations.
In contrast:
* A. Key phrase extraction identifies main ideas in text and is used in content summarization, not in response optimization.
* B. Sentiment analysis detects emotional tone (positive, negative, neutral), but it doesn't refine QnA responses.
* C. Business logic defines operational rules in an application, not machine learning-driven feedback.
The AI-900 guide specifically emphasizes that QnA Maker supports active learning to improve the quality of answers based on end-user feedback, making this the verified and official Microsoft answer.
Reference (from Microsoft Learn AI-900 content):
"Active learning uses feedback from end users to automatically suggest improvements to a knowledge base, helping improve the accuracy of answers over time."
NEW QUESTION # 181
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:
This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.
NEW QUESTION # 182
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