AI-900 Describe Artificial Intelligence workloads and considerations Practice Test 9 — 15 Questions

Question 1

What does the responsible AI principle of 'human in the loop' refer to?

Accepted Answer

Maintaining human oversight and the ability to review or override consequential AI decisions. The 'human in the loop' principle ensures that humans maintain meaningful oversight over AI systems, particularly for high-stakes or consequential decisions. This means humans can review, override, or intervene in AI-generated outputs, preventing fully automated decision-making in critical scenarios such as medical diagnosis, loan approvals, or criminal justice. It is a core component of responsible AI, balancing automation with accountability.

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A requirement for humans to manually enter all data into AI systems

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Training AI models using feedback from human labelers only

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Requiring users to prove they are human before using AI services

Question 2

What is the primary difference between supervised and unsupervised machine learning?

Accepted Answer

Supervised uses labeled training data; unsupervised finds patterns in unlabeled data. The primary difference between supervised and unsupervised machine learning is that supervised learning uses labeled training data to learn a mapping from inputs to outputs, while unsupervised learning finds hidden patterns or structures in unlabeled data without predefined labels. This distinction is fundamental to choosing the right approach for a given problem, such as classification (supervised) versus clustering (unsupervised).

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Supervised learning requires human oversight; unsupervised runs automatically

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Supervised is for images; unsupervised is for text

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Supervised is older and less accurate than unsupervised

Question 3

A logistics company wants to use AI to automatically sort packages based on their destination address printed on the label. Which AI workload combination is needed?

Accepted Answer

OCR to read the printed address and classification to determine routing. The scenario requires two distinct AI workloads: OCR (Optical Character Recognition) to extract the printed destination address from the package label, and classification to map that address to the correct routing category (e.g., zip code, region, or delivery route). This combination directly solves the problem of reading unstructured text and assigning it to a predefined output class.

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Reinforcement learning to optimize package sorting speed

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Facial recognition to identify the delivery person

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Sentiment analysis to assess package condition

Question 4

What is 'explainable AI' (XAI) and why is it important for responsible AI?

Accepted Answer

Techniques that make AI decision-making understandable to humans, supporting transparency and trust. Explainable AI (XAI) refers to a set of techniques and methods that produce human-understandable explanations of AI model decisions, outputs, and behaviors. It is critical for responsible AI because it enables transparency, builds user trust, supports regulatory compliance (e.g., GDPR's right to explanation), and helps identify and mitigate bias or errors in model predictions.

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AI systems that can explain jokes and riddles to users

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AI systems designed to teach other AI systems

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AI that automatically generates explanations of its errors

Question 5

What is a bot in the context of Azure Bot Service?

Accepted Answer

A software program that engages in natural language conversations with users. In the context of Azure Bot Service, a bot is a software program that uses natural language processing (NLP) to engage in conversational interactions with users. It leverages the Bot Framework SDK and can be integrated with channels like Microsoft Teams, Slack, or web chat to handle dialogues, answer questions, or perform tasks through text or speech.

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An automated robot that performs physical tasks in manufacturing

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A malicious program that attacks websites automatically

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An automated script for data backup in Azure storage

Question 6

What is computer vision and give three real-world application examples.

Accepted Answer

Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation. Computer vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world, such as images and videos. The three examples given—autonomous driving (e.g., detecting pedestrians and lane markings), medical imaging (e.g., analyzing X-rays for tumors), and retail automation (e.g., self-checkout systems recognizing products)—are classic real-world applications that demonstrate the breadth of computer vision beyond simple text recognition.

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Computer vision is limited to text recognition only; it cannot detect objects

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Computer vision only works on satellite imagery for geographic analysis

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Computer vision requires extremely expensive hardware unavailable in the cloud

Question 7

What does it mean for an AI system to be 'inclusive' according to Microsoft's responsible AI principles?

Accepted Answer

AI systems should empower all people including those with disabilities and from diverse backgrounds. Microsoft's responsible AI principle of inclusiveness requires that AI systems are designed to empower everyone, including people with disabilities and those from diverse cultural, linguistic, and socioeconomic backgrounds. This means the system should account for accessibility needs (e.g., screen readers, voice input) and avoid biases that could exclude or disadvantage any group.

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AI systems should include as many features as possible regardless of user needs

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AI data should include examples from every country in the world

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All employees should be included in AI model training decisions

Question 8

What is the difference between a chatbot and a conversational AI agent?

Accepted Answer

Chatbots use fixed rules/decision trees; conversational AI uses NLP/ML for flexible, context-aware responses. Chatbots traditionally rely on predefined rules or decision trees to handle user inputs, limiting them to scripted interactions. In contrast, conversational AI agents leverage natural language processing (NLP) and machine learning (ML) to understand intent, manage context, and generate dynamic, human-like responses. This allows conversational AI to handle ambiguous phrasing, maintain multi-turn dialogue state, and adapt to user behavior without explicit programming for every scenario.

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Chatbots are always voice-based; conversational AI is text-only

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Chatbots are more expensive to build than conversational AI

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They are the same technology with different marketing terms

Question 9

What is natural language processing (NLP)?

Accepted Answer

A branch of AI that enables computers to understand and generate human language. Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to read, interpret, generate, and respond to text or speech in a way that is both meaningful and contextually relevant, using techniques such as tokenization, part-of-speech tagging, named entity recognition, and language modeling.

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The process of converting programming language code into machine code

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A networking protocol for processing data transmissions

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A type of database query language for natural language questions

Question 10

Which responsible AI principle requires that AI systems have mechanisms for people to raise concerns and seek redress?

Accepted Answer

Accountability. The Accountability principle in responsible AI ensures that AI systems are designed with mechanisms for human oversight, feedback, and redress. This includes providing clear channels for users to raise concerns about system behavior and seek remedies for any harm caused. Microsoft's responsible AI framework explicitly ties accountability to the ability to audit, review, and contest AI decisions.

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Transparency

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Reliability

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Fairness

Question 11

What is the difference between narrow AI and general AI?

Accepted Answer

Narrow AI excels at one specific task; general AI would have human-like intelligence across all domains. Narrow AI (also called weak AI) is designed and trained to perform a single specific task, such as image recognition or language translation, while general AI (strong AI) would possess the ability to understand, learn, and apply intelligence across a wide range of tasks at a human-like level. General AI remains a theoretical concept and has not been achieved, whereas narrow AI powers virtually all current AI systems, including those on Azure like Computer Vision and Language Understanding (LUIS).

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Narrow AI is more powerful than general AI

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Narrow AI runs on-premises; general AI runs in the cloud

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Narrow AI is for businesses; general AI is for consumers

Question 12

A retail company wants to predict which customers are likely to cancel their subscription in the next 30 days. What ML task type is this?

Accepted Answer

Binary classification to predict whether each customer will cancel or stay. This is a binary classification task because the goal is to predict one of two mutually exclusive outcomes for each customer: either they will cancel (churn) or stay (not churn) within the next 30 days. Binary classification algorithms, such as logistic regression or decision trees, are specifically designed to assign each input to one of two discrete labels based on learned patterns from historical data.

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Clustering to identify similar customer segments

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Regression to predict the customer's lifetime value

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Generative AI to write personalized retention emails

Question 13

What is the purpose of Microsoft's 'Responsible AI Impact Assessment'?

Accepted Answer

A pre-deployment framework for identifying and mitigating potential AI harms. The Responsible AI Impact Assessment is a pre-deployment framework designed to help organizations identify, document, and mitigate potential harms associated with AI systems before they are released. It aligns with Microsoft's responsible AI principles, such as fairness, reliability, privacy, and transparency, ensuring that risks are systematically addressed rather than measured after deployment.

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A performance benchmark measuring AI response times

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A financial model for calculating AI project ROI

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A testing framework for measuring AI model accuracy

Question 14

What is the primary benefit of using pre-built AI services (like Azure AI Vision or Azure AI Language) versus building custom ML models?

Accepted Answer

Rapid deployment with no ML expertise required — accessible via REST API. Pre-built AI services like Azure AI Vision and Azure AI Language are designed for rapid deployment without requiring machine learning expertise. They provide REST API endpoints that developers can call directly to integrate capabilities like image analysis or sentiment analysis into applications, eliminating the need to train, manage, or deploy custom models.

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Pre-built services always outperform custom models for any task

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Pre-built services are always free, unlike custom ML models

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Pre-built services work offline without internet connectivity

Question 15

What is prompt injection and why is it a security concern for AI systems?

Accepted Answer

A security attack where malicious inputs try to override AI system instructions or bypass guardrails. Prompt injection is a security attack where a malicious user crafts input that overrides the AI system's original instructions or bypasses its guardrails, causing the model to behave in unintended ways. This is a critical security concern because it can lead to data exfiltration, unauthorized actions, or the generation of harmful content, undermining the trust and safety of AI deployments.

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A technique for making AI prompts shorter and more efficient

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The process of adding examples to prompts to improve model performance

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A method of injecting training data into a model after deployment