AI-900 Describe Artificial Intelligence workloads and considerations — Timed 10 Questions

Question 1

A multinational corporation deploys an AI-powered language translation system that performs well for English, Spanish, and French, but has significantly lower accuracy for Swahili and Navajo. The company wants to ensure the system serves all users equitably. Which Microsoft responsible AI principle is most directly relevant to this scenario?

10 minute time limit — choose an answer to begin.

Accepted Answer

Fairness. Fairness. This principle directly addresses AI systems that treat all people equitably and without bias. The translation system has significantly lower accuracy for Swahili and Navajo, which are underrepresented in the training data, creating an unfair bias against speakers of those languages. The goal of ensuring equitable service calls for applying the fairness principle to reduce this disparity.

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Inclusiveness

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Reliability and safety

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Transparency

Question 2

A hospital uses an AI system to prioritize patient appointments based on urgency. The system is trained on historical data. The team wants to ensure that the system does not discriminate against patients based on age or disability. Which Microsoft responsible AI principle should most directly guide the design of this system?

Accepted Answer

Transparency. The scenario of avoiding discrimination based on age or disability is a fairness concern, which in Microsoft's responsible AI framework is addressed by the Transparency principle. Transparency ensures that AI systems are explainable and understandable, allowing stakeholders to audit decisions for bias and unfair outcomes. By making the prioritization process transparent, the hospital can identify and mitigate discriminatory patterns, promoting fairness indirectly through accountability and oversight.

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Inclusiveness

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Privacy and security

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Reliability and safety

Question 3

A financial institution uses an AI model to approve loan applications. A customer is denied a loan and requests an explanation for the decision. The development team cannot explain how the model reached its conclusion because the model is a deep neural network with complex layers. Which Microsoft responsible AI principle is being violated in this scenario?

Accepted Answer

Fairness. The scenario describes a model whose internal reasoning cannot be explained by the development team. This lack of explainability makes it impossible to verify whether the model is making unbiased decisions, directly violating the Microsoft responsible AI principle of Fairness. Fairness requires that AI systems treat all people fairly and avoid discrimination. Without understanding how the model reaches its conclusions, the organization cannot ensure that it is not unfairly denying loans based on protected attributes.

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Accountability

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Reliability and Safety

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Privacy and Security

Question 4

Which AI workload type attempts to predict a continuous numeric value based on input features?

Accepted Answer

Regression. Regression is the correct AI workload type because it is specifically designed to predict a continuous numeric value (e.g., price, temperature, sales amount) based on input features. Unlike classification, which predicts discrete categories, regression models output a real number by learning a mapping function from the input variables to a continuous target variable, often using algorithms like linear regression, decision trees, or neural networks.

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Classification

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Clustering

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Anomaly detection

Question 5

What is 'sentiment analysis' used for in business applications?

Accepted Answer

Automatically classifying customer text as positive, negative, or neutral to monitor brand and product perception. Sentiment analysis uses NLP to determine emotional tone and classify text as positive, negative, or neutral. In business, it is commonly used to monitor brand and product perception from customer feedback, making option B correct. Option D describes machine translation, not sentiment analysis.

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Detecting whether a customer is experiencing technical problems with a product

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Predicting whether a customer will make a repeat purchase based on their browsing history

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Automatically translating customer reviews into multiple languages for international support teams

Question 6

A bank is developing an AI system to automatically approve personal loans. To ensure the system does not discriminate against any group of applicants, which Microsoft responsible AI principle should the bank primarily focus on?

Accepted Answer

Fairness. Fairness is the correct principle because it directly addresses the need to prevent discrimination in AI systems, such as loan approval models. By focusing on fairness, the bank ensures that the model's predictions do not systematically disadvantage any group based on protected attributes like race, gender, or age, which is critical for ethical and legal compliance.

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Accountability

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Inclusiveness

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Reliability and Safety

Question 7

A manufacturing company uses an AI system to predict when machines will need maintenance. The system must work correctly under varying factory floor conditions such as temperature changes and noise levels. Which Microsoft responsible AI principle is most directly focused on ensuring the system performs reliably in these different conditions?

Accepted Answer

Reliability & Safety. B is correct because the Reliability & Safety principle ensures that AI systems operate consistently and predictably under varying conditions, such as temperature changes and noise levels on a factory floor. This principle mandates rigorous testing, monitoring, and fail-safe mechanisms to maintain performance and prevent harm when environmental factors deviate from expected ranges.

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Fairness

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Privacy & Security

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Inclusiveness

Question 8

A data scientist is training a credit risk model and wants to use Azure Machine Learning's Responsible AI dashboard to identify if the model is biased against a certain demographic group. Which component of the dashboard should they use to evaluate this?

Accepted Answer

Model Fairness Assessment. The Model Fairness Assessment component of Azure Machine Learning's Responsible AI dashboard is specifically designed to evaluate and mitigate bias in machine learning models. It allows data scientists to assess disparities in model performance across demographic groups defined by sensitive features (e.g., race, gender) using metrics like demographic parity, equal opportunity, and disparate impact. This directly addresses the question of identifying bias against a certain demographic group.

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Model Interpretability

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Error Analysis

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Data Balance Analysis

Question 9

A healthcare start-up proposes a fully automated AI system to diagnose patients from medical scans without any human doctor review. They claim the system is 99% accurate. According to Microsoft's responsible AI principles, which principle is most directly violated by removing human oversight from this critical decision-making process?

Accepted Answer

Accountability. Removing human oversight from a fully automated diagnostic system violates the accountability principle. Microsoft's responsible AI principle of accountability requires that humans remain responsible for AI-driven decisions, especially in high-stakes healthcare scenarios where errors can have life-or-death consequences. By eliminating any human doctor review, the start-up fails to ensure that a human can intervene, validate, or take responsibility for the system's outputs.

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Fairness

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Reliability and safety

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Transparency

Question 10

A financial services company uses an AI system to recommend personalized investment portfolios. A customer requests an explanation of why a particular investment was recommended. Which Microsoft responsible AI principle is primarily focused on ensuring the company can provide this explanation?

Accepted Answer

Transparency. Transparency is the correct principle because it directly addresses the need for AI systems to be understandable and interpretable. In this scenario, the customer's request for an explanation of a specific investment recommendation requires the AI to provide clear reasoning for its output, which is the core of transparency. This principle ensures that the company can explain how and why a decision was made, building trust and enabling oversight.

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Accountability

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Fairness

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Reliability