AI-900 Describe Artificial Intelligence workloads and considerations Practice Test 7 — 15 Questions

Question 1

A company deploys an AI system to screen job resumes. The system consistently rejects candidates from a certain university, but the company cannot determine which features led to the decision or how the model arrived at that outcome. Which Microsoft responsible AI principle is most directly violated?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Transparency. The scenario describes a system that makes decisions without providing any insight into how or why those decisions were made. Transparency, as a Microsoft responsible AI principle, requires that AI systems be understandable and that their decisions can be explained. Since the company cannot determine which features led to the rejection or how the model arrived at the outcome, the lack of explainability directly violates the Transparency principle.

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Fairness

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Accountability

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Privacy and security

Question 2

A city council deploys an AI system to analyze surveillance footage and automatically issue traffic violation fines. They want to ensure the system does not disproportionately target one type of vehicle (e.g., bicycles over cars) when issuing fines. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. The scenario describes a risk of algorithmic bias where the AI system might disproportionately issue fines to bicycles over cars. The Microsoft responsible AI principle of Fairness directly addresses this by requiring that AI systems treat all groups equitably and avoid discrimination based on protected attributes. Ensuring fairness involves auditing the model's predictions across different vehicle types and mitigating any statistical disparities.

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Inclusiveness

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Reliability and safety

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Transparency

Question 3

A financial services company uses an AI system to detect fraudulent credit card transactions. After deployment, the system incorrectly flags a significant number of legitimate transactions as fraudulent, causing customer dissatisfaction. The company wants to reduce these false positives while still catching most fraudulent transactions. Which Microsoft responsible AI principle should guide their redesign of the system?

Accepted Answer

Reliability and safety. The Reliability and safety principle emphasizes that AI systems should perform reliably, safely, and consistently under normal conditions. In this scenario, the high rate of false positives indicates the system is not operating reliably for legitimate transactions, causing customer harm. Redesigning to reduce false positives while maintaining fraud detection aligns directly with improving the system's reliability and safety for end users.

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Fairness

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Transparency

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Privacy and security

Question 4

A company uses an AI system to help screen job applications. The system ranks candidates based on their resumes. The company wants to ensure that if a candidate asks why they were not selected, the company can provide a clear explanation of the factors that influenced the AI's decision. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the company needs to provide a clear explanation of why a candidate was not selected, which directly aligns with transparency's goal of making AI decisions explainable to users.

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Accountability

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Privacy and security

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Reliability and safety

Question 5

A bank uses an AI system to approve personal loans. Some customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide these explanations?

Accepted Answer

Transparency. Transparency is the correct principle because it requires AI systems to be understandable and for decisions to be explainable to users. In this scenario, customers denied loans have a right to know why the AI made that decision, which aligns with Microsoft's principle of transparency—ensuring that AI systems communicate their purpose, limitations, and reasoning in clear, accessible terms.

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Fairness

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Reliability and safety

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Accountability

Question 6

A hospital deploys an AI system to assist with diagnosing diseases from medical images. A doctor disagrees with the system's diagnosis and overrules it. The hospital wants to document this interaction for legal and audit purposes. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Accountability. The scenario involves documenting a human override of an AI system's diagnosis for legal and audit purposes, which directly relates to accountability. Accountability in responsible AI ensures that organizations can answer for their AI systems' decisions by maintaining clear records of interactions, including when humans overrule AI outputs. This principle requires traceability and governance mechanisms, such as audit trails, to assign responsibility for outcomes.

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Fairness

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Reliability and safety

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Transparency

Question 7

A medical research organization uses an AI system to analyze patient health records to identify patterns in disease progression. They publish a research paper that includes tables of aggregated statistics derived from the data. Later, a researcher discovers that by combining multiple statistics, it is possible to identify individual patients. Which Microsoft responsible AI principle has been most directly compromised?

Accepted Answer

Privacy and security. The scenario describes a re-identification attack, where aggregated statistics (tables) can be combined to infer individual patient identities. This directly violates the privacy and security principle, which requires that AI systems protect personal data and prevent unauthorized identification. Microsoft's responsible AI principle of privacy and security emphasizes safeguarding data through techniques like differential privacy, which was not applied here.

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Fairness

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Transparency

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Accountability

Question 8

A company uses an AI system to automatically generate personalized email subject lines for marketing campaigns. The system has been trained on historical data that includes biased language patterns. The company wants to ensure the generated subject lines do not reinforce stereotypes based on gender, age, or ethnicity. Which Microsoft responsible AI principle should guide the selection and filtering of training data?

Accepted Answer

Inclusiveness. Inclusiveness, because this principle directly addresses the need to ensure AI systems treat all people fairly and avoid reinforcing stereotypes. By selecting and filtering training data to remove biased language patterns related to gender, age, or ethnicity, the company operationalizes inclusiveness to prevent the model from generating discriminatory subject lines. This principle guides the proactive mitigation of bias in data curation and model outputs.

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Reliability and safety

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Privacy and security

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Transparency

Question 9

A hospital deploys an AI system that predicts patient readmission risk within 30 days of discharge. The model uses features such as age, medical history, and treatment plans. The hospital discovers that the model has a significantly higher false positive rate for patients of a certain ethnic group compared to others, even though the model's overall accuracy is similar across groups. This disparity was not intentional. Which Microsoft responsible AI principle is most directly compromised?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.

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Reliability and safety

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Privacy and security

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Accountability

Question 10

An autonomous delivery robot uses AI to navigate sidewalks. The robot occasionally fails to detect pedestrians in low-light conditions, leading to near-collisions. The company wants to ensure the system is robust and safe before wider deployment. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Reliability and safety. The robot's failure to detect pedestrians in low-light conditions directly impacts the system's ability to operate reliably and safely. The Reliability and safety principle in Microsoft's responsible AI framework requires that AI systems perform consistently under expected conditions and fail gracefully when they cannot. Ensuring the robot can handle edge cases like low light is a core safety requirement before deployment.

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Fairness

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Privacy and security

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Transparency

Question 11

A bank uses an AI system to approve or deny personal loan applications. Several customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide understandable reasons for the AI's decision?

Accepted Answer

Transparency. Transparency is the Microsoft responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the bank must provide clear, understandable reasons for loan denials, which directly aligns with transparency's goal of enabling users to understand how and why decisions are made. This principle ensures that AI outcomes are not opaque black-box decisions but can be explained in human terms.

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Reliability and safety

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Fairness

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Privacy and security

Question 12

A hospital uses an AI system to recommend treatment plans for patients. The system's decision process is complex and not easily understood by doctors. The hospital wants to ensure that doctors can trust and verify the system's recommendations. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. The scenario describes a complex AI decision process that doctors cannot easily understand, which directly relates to the need for interpretability and openness about how the system works. Transparency is the Microsoft responsible AI principle that focuses on making AI systems understandable and providing clear documentation, so users can verify and trust the outputs. By ensuring transparency, the hospital can enable doctors to audit the reasoning behind treatment recommendations, fostering trust and accountability.

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Reliability and safety

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Fairness

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Privacy and security

Question 13

What is artificial intelligence (AI) in the context of computer science?

Accepted Answer

Software that enables machines to simulate human intelligence and learn from data. Artificial intelligence (AI) in computer science refers to software systems that can perform tasks typically requiring human intelligence, such as learning from data, reasoning, and decision-making. This definition encompasses machine learning, deep learning, and other subfields where models are trained on data to improve performance over time, rather than following explicitly programmed rules.

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A type of computer hardware that processes data faster than traditional CPUs

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A programming language used to write algorithms

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A type of database that stores structured information

Question 14

Which of the following is an example of an AI workload that uses prediction?

Accepted Answer

Predicting equipment failure based on sensor data. Predicting equipment failure based on sensor data is a classic example of a predictive AI workload. It uses historical sensor data (e.g., temperature, vibration, pressure) to train a machine learning model that forecasts when equipment is likely to fail, enabling proactive maintenance. This falls under the AI workload category of prediction, where the model outputs a future outcome or probability.

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Displaying a list of items in alphabetical order

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Storing customer records in a database

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Formatting text documents

Question 15

Which AI workload type is used when a system needs to automatically organize unstructured data into meaningful groups without predefined categories?

Accepted Answer

Clustering. Clustering is the correct AI workload type because it is an unsupervised learning technique that automatically groups unstructured data into meaningful clusters based on inherent similarities, without requiring predefined categories or labeled training data. This makes it ideal for tasks like customer segmentation, document organization, or anomaly detection where the natural structure of the data is unknown.

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Classification

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Regression

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Object detection