AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company develops an AI system to predict employee performance based on work habits. The system uses complex neural networks and its decisions are not easily interpretable. The company wants to ensure that employees can understand why a particular performance prediction was made. Which Microsoft responsible AI principle is most directly relevant?
Why wrong: Fairness ensures that AI systems treat all groups equitably, but the question is about understanding the reasoning behind predictions, not about bias.
B
B) Reliability and safety
Why wrong: Reliability and safety ensure that the system operates consistently and safely, but they do not directly address the explainability of decisions.
C
C) Transparency
Transparency requires that AI systems be interpretable and that their decisions can be explained to users and stakeholders. This directly matches the company's goal of allowing employees to understand why a prediction was made.
D
D) Privacy and security
Why wrong: Privacy and security focus on protecting data from unauthorized access and misuse, not on explaining model decisions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
C) Transparency
Transparency is the responsible AI principle that directly addresses the need for interpretability and explainability of AI systems. In this scenario, the company uses complex neural networks that are inherently black-box models, making their decisions difficult to understand. Transparency requires that the system provides explanations for its predictions, enabling employees to comprehend why a particular performance rating was assigned, which aligns with the goal of building trust and accountability.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
A) Fairness
Why it's wrong here
Fairness ensures that AI systems treat all groups equitably, but the question is about understanding the reasoning behind predictions, not about bias.
✗
B) Reliability and safety
Why it's wrong here
Reliability and safety ensure that the system operates consistently and safely, but they do not directly address the explainability of decisions.
✓
C) Transparency
Why this is correct
Transparency requires that AI systems be interpretable and that their decisions can be explained to users and stakeholders. This directly matches the company's goal of allowing employees to understand why a prediction was made.
Related concept
Read the scenario before looking for a memorised answer.
✗
D) Privacy and security
Why it's wrong here
Privacy and security focus on protecting data from unauthorized access and misuse, not on explaining model decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'transparency' with 'fairness' because both involve ethical AI, but transparency specifically addresses the 'why' behind a decision, not the absence of bias.
Detailed technical explanation
How to think about this question
Under the hood, transparency in AI often involves techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to approximate the decision boundary of a complex neural network and highlight feature importance for a specific prediction. In a real-world HR scenario, an employee might see that their prediction was influenced heavily by 'overtime hours' and 'project completion rate' rather than 'years of experience', allowing them to understand and potentially adjust their behavior. This principle is distinct from model interpretability, which is a property of simpler models like linear regression, whereas transparency is a broader commitment to providing explanations even for black-box systems.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: C) Transparency — Transparency is the responsible AI principle that directly addresses the need for interpretability and explainability of AI systems. In this scenario, the company uses complex neural networks that are inherently black-box models, making their decisions difficult to understand. Transparency requires that the system provides explanations for its predictions, enabling employees to comprehend why a particular performance rating was assigned, which aligns with the goal of building trust and accountability.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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