AI-900 Describe Artificial Intelligence workloads and considerations Practice Test 3 — 15 Questions

Question 1

A bank deploys an AI system to automatically approve or reject loan applications. After six months, an audit reveals that the system approves loans at a significantly lower rate for applicants from a specific ethnic group compared to other groups with similar financial profiles. Which Microsoft responsible AI principle is most directly violated by this outcome?

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

Accepted Answer

Fairness. The AI system's approval rate disparity for a specific ethnic group, despite similar financial profiles, directly violates the Fairness principle. Fairness requires that AI systems treat all groups equitably and avoid discrimination based on sensitive attributes like ethnicity. This outcome demonstrates a lack of fairness in the model's decision-making process.

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Transparency

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Privacy

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Reliability

Question 2

A development team creates an AI chatbot for a hospital website that answers patient queries. The team scripts the AI to always respond with a disclaimer that it is not a substitute for professional medical advice. Additionally, they include a mechanism for users to report inaccurate responses, which are then reviewed by a human team. Which Microsoft responsible AI principle is most directly being implemented by the reporting and human review mechanism?

Accepted Answer

Accountability. The reporting and human review mechanism directly implements the Accountability principle, which requires that AI systems be designed with clear lines of responsibility and oversight. By allowing users to flag inaccuracies and having a human team review those reports, the organization takes ownership of the system's outputs and ensures corrective actions can be taken. This goes beyond mere transparency or reliability—it establishes a feedback loop where humans remain ultimately responsible for the AI's behavior.

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Fairness

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Reliability and safety

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Transparency

Question 3

A company deploys an AI system to screen job resumes and rank candidates. The company wants to ensure that candidates can understand how the system arrived at its decisions. Which Microsoft responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Transparency. The requirement that candidates can understand how the AI system arrived at its decisions directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that their decision-making processes be explainable to users. In the context of resume screening, this means providing clear reasoning for why a candidate was ranked a certain way, such as highlighting which features (e.g., skills, experience) most influenced the score.

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Fairness

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Reliability and Safety

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Privacy and Security

Question 4

A university uses an AI system to screen scholarship applications. The system was trained on historical data that mostly awarded scholarships to students from STEM majors. Consequently, the system consistently gives lower scores to equally qualified students from humanities and arts majors. Which Microsoft responsible AI principle is most directly being violated by this outcome?

Accepted Answer

Fairness. The AI system's training data caused it to learn a biased pattern that systematically disadvantages humanities and arts applicants, which directly violates the fairness principle. Fairness in responsible AI requires that systems treat all groups equitably and do not perpetuate or amplify existing biases, especially when making high-stakes decisions like scholarship awards.

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Reliability and safety

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Privacy and security

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Transparency

Question 5

A company develops an AI system to screen job resumes and rank candidates for interviews. The system is trained on historical hiring data that favored candidates from certain well-known universities. The company decides to deploy the system without any adjustments to address this bias. Which Microsoft responsible AI principle is most directly being violated?

Accepted Answer

Fairness. (Fairness) because the AI system was trained on biased historical data that favored candidates from certain universities, and deploying it without adjustments directly violates the fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on protected attributes such as educational background. By not mitigating the bias, the system perpetuates historical inequities in the hiring process.

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Inclusiveness

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Reliability and Safety

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Privacy and Security

Question 6

A company develops an autonomous vehicle AI system. The system was trained exclusively on data from sunny, dry weather conditions. When the vehicles are deployed in a region that experiences frequent snow and fog, the system fails to correctly identify obstacles, leading to safety risks. Which Microsoft responsible AI principle is most directly violated by this deployment?

Accepted Answer

Reliability and safety. The system fails in snow and fog because it was trained only on sunny, dry data, making it unreliable in those conditions. The Microsoft responsible AI principle of Reliability and safety requires AI systems to perform consistently and safely across their intended deployment environments. Deploying without testing for diverse weather violates this principle by exposing users to safety risks.

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Fairness

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Transparency

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Privacy and security

Question 7

A healthcare company deploys an AI system to assist doctors in diagnosing skin conditions from images. The system is a deep neural network that does not provide explanations for its predictions. The company implements a process where every AI recommendation is logged, and a medical team reviews any adverse outcomes to determine if the system or a human made an error. The company also clearly assigns responsibility for the system's outputs to a specific clinical oversight committee. Which Microsoft responsible AI principle is most directly being implemented by these actions?

Accepted Answer

Accountability. The actions described—logging AI recommendations, reviewing adverse outcomes to determine error source, and assigning a clinical oversight committee—directly implement the Accountability principle. Accountability requires that organizations clearly assign responsibility for AI system outputs and have processes to review and address impacts, which is exactly what the company does by creating a human review loop and a designated committee.

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Fairness

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Reliability and safety

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Transparency

Question 8

A company builds an AI system to filter job applications and rank candidates. The system is trained on historical hiring data. To reduce potential bias, the company removes protected attributes such as gender and ethnicity from the training data. However, after deployment, the system still shows a statistically significant bias against female candidates. Which Microsoft responsible AI principle most directly requires the company to investigate and address this remaining bias, even when protected attributes are removed?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all people fairly and avoid creating or reinforcing discriminatory outcomes. Even when protected attributes like gender are removed from training data, bias can persist through proxy variables (e.g., zip code, education history) that correlate with protected attributes. The company must investigate and mitigate this remaining bias because Fairness mandates proactive assessment and correction of disparate impact, not just removal of obvious features.

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Inclusiveness

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Reliability and safety

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Transparency

Question 9

A hospital uses an AI system to analyze patient records for research. To protect patient identities, the system should not store or transmit any personally identifiable information (PII) outside the secure network. Which responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Privacy and Security. The requirement to not store or transmit PII outside the secure network directly addresses the Privacy and Security principle. This principle ensures that individuals' data is protected from unauthorized access or exposure, which is critical in healthcare AI systems handling sensitive patient records. By restricting PII to the secure network, the system upholds data confidentiality and compliance with regulations like HIPAA.

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Fairness

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Inclusiveness

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Reliability and Safety

Question 10

An insurance company uses an AI system to automatically process and approve or reject claims. The system sometimes rejects valid claims because the uploaded documents are in slightly different formats (e.g., PDF vs. scanned images). The company wants to minimize these errors. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?

Accepted Answer

Reliability and safety. The issue is that the AI system fails to process valid claims due to variations in document formats (PDF vs. scanned images), which is a reliability and safety problem. The system should be robust enough to handle input variations and consistently produce correct outcomes. Microsoft's Reliability and safety principle focuses on ensuring AI systems operate reliably, safely, and consistently under expected conditions, directly addressing the need to minimize such errors.

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Fairness

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Inclusiveness

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Transparency

Question 11

A company develops an AI system to screen job candidates based on their resumes. The system is trained on historical data. Analysis reveals that the model has an adverse impact against female candidates due to a proxy feature (e.g., 'years of continuous employment') that correlates with gender. The team removes the protected attribute 'gender' from the training data but the biased outcome persists. According to Microsoft's responsible AI principles, which additional step should the team take to address this unfairness?

Accepted Answer

Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.. Microsoft's responsible AI principle of fairness requires not just removing protected attributes but also detecting and mitigating proxy features that cause bias. Fairlearn is a Microsoft open-source toolkit specifically designed to assess and mitigate unfairness in AI systems, offering algorithms like 'Exponentiated Gradient Reduction' or 'Grid Search' to reduce disparity while preserving model performance. Simply removing the proxy feature (A) may not always be feasible if it carries predictive value, and Fairlearn provides a systematic way to balance fairness and accuracy.

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Remove the offending proxy feature 'years of continuous employment' from the training data.

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Train a separate model for each gender group to ensure equal outcomes.

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Collect more training data from underrepresented groups.

Question 12

A hospital deploys an AI system to assist in diagnosing diseases from medical images. The system is a complex deep learning model that provides a diagnosis without any explanation. Doctors are skeptical and want to understand why the system made a particular recommendation. The hospital decides to deploy the system without providing any interpretability. Which Microsoft responsible AI principle is most directly being violated?

Accepted Answer

Transparency. The system provides a diagnosis without any explanation of how it reached its conclusion, and the hospital decides to deploy it without interpretability. This directly violates the transparency principle, which requires AI systems to be understandable and for their decisions to be explainable to users, especially in high-stakes domains like healthcare.

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Fairness

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Reliability & Safety

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Inclusiveness

Question 13

A company develops an AI system to recommend personalized news articles to users. The system uses collaborative filtering, suggesting articles that similar users have read. Which type of machine learning does this approach primarily rely on?

Accepted Answer

Unsupervised learning. Collaborative filtering identifies patterns in user-item interactions without labeled outcomes, grouping users or items based on similarity. This is a classic unsupervised learning task because the system discovers hidden structures (e.g., user clusters) from unlabeled data, rather than being trained on explicit input-output pairs.

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Supervised learning

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Reinforcement learning

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Semi-supervised learning

Question 14

A hospital uses an AI system to prioritize emergency room patients based on severity. The system was trained on historical data that may contain biases against certain demographic groups. The hospital wants to ensure the system does not disproportionately disadvantage any group. According to Microsoft's responsible AI principles, which practice should the hospital implement during the design phase?

Accepted Answer

Conduct an impact assessment and involve diverse stakeholders during design. Microsoft's responsible AI principles emphasize the importance of conducting impact assessments and involving diverse stakeholders during the design phase to identify and mitigate potential biases before deployment. This proactive approach aligns with the fairness principle, ensuring that the AI system does not disproportionately disadvantage any demographic group. Simply removing features or relying on post-deployment monitoring is insufficient to address systemic biases embedded in historical data.

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Remove all demographic features from the training data to achieve fairness through unawareness

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Use a complex, uninterpretable model to avoid scrutiny of predictions

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Deploy the system and rely on post-deployment monitoring to catch unfair outcomes

Question 15

A retail store wants to use an AI solution to automatically monitor security camera feeds and detect when a shelf is empty or if a person is in a restricted area. Which type of AI workload is best suited for this task?

Accepted Answer

Computer Vision. Computer Vision is the correct AI workload because it enables the system to analyze video frames from security cameras to detect visual patterns such as empty shelves (object absence) or unauthorized persons in restricted areas (object presence and location). This workload uses image classification, object detection, and semantic segmentation to interpret visual data in real time.

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Natural Language Processing

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Speech Recognition

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Anomaly Detection