AI-900 Describe Artificial Intelligence workloads and considerations Practice Test 13 — 15 Questions

Question 1

What is 'energy and sustainability' as an AI application area?

Accepted Answer

Using AI to optimise energy grids, building efficiency, agriculture, and climate modelling. 'energy and sustainability' as an AI application area refers to using AI to solve environmental challenges, such as optimizing energy grids, improving building efficiency, enhancing agricultural yields, and advancing climate modeling. This aligns with Microsoft's definition of AI for sustainability, where AI models analyze data to reduce waste, predict energy demand, and support renewable integration. It is not about the energy cost of AI itself, but about applying AI to broader sustainability goals.

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Measuring and reducing the energy consumed by AI model training itself

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Powering AI data centres with 100% renewable energy sources

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Creating AI models that require less energy to run than traditional algorithms

Question 2

What is 'customer churn prediction' as an AI workload and what ML type does it use?

Accepted Answer

Using supervised classification to predict which customers are likely to cancel or become inactive. Customer churn prediction is a supervised machine learning workload where historical customer data (e.g., usage patterns, support interactions, billing history) is used to train a classification model. The model learns to assign a binary label (churn or not churn) to new customers, making it a supervised classification task. This directly matches option B, which correctly identifies the use of supervised classification to predict likely churners.

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Analysing customer complaints to identify the root cause of service dissatisfaction

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Detecting when a customer has already churned based on their last login date

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Using NLP to understand why customers write negative reviews before leaving

Question 3

What is 'AI in education' and how are Azure AI services applied to learning?

Accepted Answer

Personalised learning, pronunciation assessment, automated grading, tutoring chatbots, and accessibility tools. It accurately describes the application of Azure AI services to education, including personalized learning via Azure Machine Learning, pronunciation assessment with Azure Speech Services, automated grading using Azure Cognitive Services, tutoring chatbots built with Azure Bot Service, and accessibility tools leveraging Azure Cognitive Services like Computer Vision and Text-to-Speech. These services enhance learning without replacing human educators.

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Using AI to replace teachers in classrooms with fully automated instruction

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Generating educational certificates automatically when students complete online courses

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Using AI to monitor student attention levels during online classes via webcam

Question 4

What is 'model cards' in responsible AI and what information do they contain?

Accepted Answer

Transparency documents describing a model's intended use, training data, performance, biases, and limitations. Model cards are transparency documents that accompany machine learning models to disclose their intended use, training data, performance metrics, known biases, and limitations. They are a key responsible AI practice, mandated by frameworks like Microsoft's Responsible AI Standard, to ensure stakeholders understand a model's capabilities and risks before deployment.

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Azure billing documents showing the monthly cost of running a model in production

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Technical specification sheets for AI hardware accelerators used in model training

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Playing cards used in gamification of AI training to motivate data labellers

Question 5

What is 'AI for accessibility' and what Azure AI services support it?

Accepted Answer

Using speech, vision, and language AI to remove barriers for people with disabilities. 'AI for accessibility' refers to using AI technologies—specifically speech, vision, and language services—to create inclusive solutions that remove barriers for people with disabilities. Azure AI services such as Azure Cognitive Services (e.g., Computer Vision for image descriptions, Speech-to-Text for real-time captioning, and Translator for language translation) directly enable these accessibility scenarios, aligning with Microsoft's commitment to inclusive design.

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Making AI services accessible to small businesses through affordable pricing

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Providing accessible APIs with clear documentation for developer communities

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Ensuring AI applications work on low-bandwidth connections in developing regions

Question 6

What is 'credit scoring' as an AI workload and what responsible AI concerns does it raise?

Accepted Answer

ML for predicting loan repayment risk — with fairness, bias, and explainability concerns. Credit scoring in AI refers to machine learning models that predict the likelihood of a borrower repaying a loan. This raises responsible AI concerns around fairness (e.g., models may discriminate against protected groups), bias (e.g., training data may reflect historical inequalities), and explainability (e.g., complex models like gradient-boosted trees are often black boxes, making it hard to justify decisions to regulators or customers).

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A system for automatically assigning credit scores to software bugs in a development backlog

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Monitoring whether a customer has used all their credit within an approved limit

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An internal system for scoring the quality of AI projects within an organisation

Question 7

What is 'autonomous vehicles' AI and what AI technologies do they combine?

Accepted Answer

Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI. Autonomous vehicles represent a complex AI workload that integrates multiple AI technologies to perceive the environment, understand context, and make safe driving decisions. Option B is correct because it specifically lists the core AI technologies—computer vision for object detection, sensor fusion for combining data from cameras, LiDAR, and radar, localization for precise positioning, behavior prediction for anticipating actions of other road users, and path planning for determining the optimal route—that are essential for a vehicle to operate without human intervention.

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AI that automatically controls traffic lights to reduce congestion at intersections

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AI that automatically parallel parks a car using sensors and pre-programmed rules

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Using AI to optimise traffic routing in GPS navigation applications

Question 8

What is 'agent orchestration' in multi-agent AI systems?

Accepted Answer

Coordinating multiple AI agents — planning tasks, delegating to specialists, and synthesising outputs. Agent orchestration in multi-agent AI systems refers to the coordination of multiple AI agents, where a central orchestrator plans tasks, delegates them to specialized agents, and synthesizes their outputs into a coherent result. This is a core pattern in complex AI workflows, enabling modularity and specialization, unlike simple load balancing or code management.

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Scheduling when AI agents run to balance compute load across Azure regions

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Training a single model that can perform multiple specialised tasks simultaneously

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Organising AI agent code in a Git repository for version control

Question 9

What is 'image recognition for accessibility' and how does Microsoft's Seeing AI app use it?

Accepted Answer

An app using Azure AI Vision to describe scenes, read text, and identify objects aloud for blind users. Microsoft's Seeing AI app leverages Azure AI Vision (specifically the Computer Vision API) to perform real-time image recognition for accessibility. It describes scenes, reads text via optical character recognition (OCR), and identifies objects aloud, enabling blind or low-vision users to understand their surroundings through audio feedback.

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A feature that makes AI models accessible to users without programming expertise

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Accessibility compliance checking software that validates AI applications meet WCAG standards

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Screen reader software that makes Azure portal accessible to keyboard-only users

Question 10

What is 'AI in financial services' and what specific AI capabilities are most commonly applied?

Accepted Answer

Fraud detection, credit scoring, chatbots, KYC, sentiment analysis, and regulatory automation. It accurately lists the most common AI capabilities applied in financial services: fraud detection (using anomaly detection models), credit scoring (via supervised learning on historical data), chatbots (leveraging natural language processing), KYC (using document verification and facial recognition), sentiment analysis (applying NLP to news and social media), and regulatory automation (using rule-based AI and robotic process automation). These represent the broad, practical deployment of AI in finance, not a narrow or unrealistic use case.

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AI that automatically manages investment portfolios without any human involvement

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AI exclusively for high-frequency trading in stock markets

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Using AI to design new financial products like insurance policies and loan products

Question 11

What is 'AI democratisation' and how do Azure AI services support it?

Accepted Answer

Making AI capabilities accessible to all organisations and developers through pre-built APIs and low-code tools. AI democratisation refers to making AI capabilities accessible to a broad range of users, not just experts. Azure AI services support this by offering pre-built APIs (e.g., Computer Vision, Language Understanding) and low-code tools like Azure Machine Learning designer and Power Platform AI Builder, enabling developers and organisations with limited AI expertise to integrate AI into their applications without building models from scratch.

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Making AI governance decisions through a democratic voting process within organisations

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Ensuring AI companies are publicly listed so retail investors can participate in AI growth

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Open-sourcing all AI models so any developer can use them without licensing fees

Question 12

What is 'AI in HR' (Human Resources) and what specific applications does it enable?

Accepted Answer

CV screening, employee sentiment, attrition prediction, skills analysis, and learning recommendations. AI in HR leverages machine learning and natural language processing to automate and enhance tasks like CV screening (e.g., parsing resumes for relevant skills), employee sentiment analysis (e.g., using NLP on survey responses), attrition prediction (e.g., classification models on historical data), skills gap analysis, and personalized learning recommendations. These applications augment human decision-making rather than replacing it, aligning with common AI workloads in the HR domain.

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Using AI to fully automate all HR decisions without human involvement

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Managing employee payroll and benefits calculations using traditional database systems

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Ensuring HR documents comply with employment law using rule-based systems

Question 13

What is 'the AI-900 exam' testing you on?

Accepted Answer

Foundational knowledge of AI/ML concepts and Azure AI services — suitable for non-technical stakeholders. The AI-900 exam is designed to validate foundational knowledge of AI and machine learning concepts, along with familiarity with Azure AI services. It targets non-technical stakeholders, such as business analysts or project managers, who need to understand AI capabilities and ethical considerations without requiring hands-on coding or infrastructure skills.

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Advanced ML model development and Azure ML pipeline coding skills

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Azure infrastructure management and deployment of AI workloads using IaC tools

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Ethical AI policy writing and regulatory compliance documentation

Question 14

What is 'AI in agriculture' (precision agriculture) and what AI technologies are applied?

Accepted Answer

Crop yield prediction, pest detection, irrigation optimisation, and crop health monitoring using ML and vision. Precision agriculture leverages machine learning (ML) and computer vision to analyze data from sensors, drones, and satellites for tasks like predicting crop yields, detecting pests, optimizing irrigation, and monitoring crop health. These AI technologies enable data-driven decisions that improve efficiency and sustainability in farming.

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AI that writes farming blogs and social media content for agricultural businesses

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Automating all farming tasks with AI-powered robots that replace farm workers

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Using AI to trade agricultural commodity futures on financial markets

Question 15

What is 'AI for scientific discovery' and what examples exist in this domain?

Accepted Answer

AI accelerating breakthroughs in protein folding, drug discovery, climate modelling, and materials science. 'AI for scientific discovery' refers to the use of machine learning and deep learning models to accelerate complex scientific research, such as predicting protein structures (e.g., AlphaFold), optimizing drug candidates, improving climate models, and discovering new materials. These AI systems process vast datasets and simulate molecular interactions far faster than traditional methods, enabling breakthroughs that would otherwise take years.

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AI models that write scientific papers automatically without human researchers

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Using AI to ensure scientific publications meet journal formatting requirements

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AI systems for managing scientific equipment bookings and lab resources