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 bank deploys an AI system that uses a deep neural network to approve personal loan applications. A customer whose loan was rejected requests a detailed explanation of why the decision was made. The bank's AI team realizes that the model's internal workings are too complex to provide a simple, understandable reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this situation?
Why wrong: Fairness is about avoiding bias and ensuring equitable outcomes; while important, the core issue here is explainability, not bias.
B
Transparency
Transparency ensures that AI systems are understandable and that decisions can be explained to users, which is directly missing in this case.
C
Reliability & Safety
Why wrong: Reliability & Safety ensures the system operates correctly and safely under various conditions, not specifically about providing explanations.
D
Privacy & Security
Why wrong: Privacy & Security protects personal data from unauthorized access or misuse; the issue here is about explainability, not data protection.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transparency
The bank's inability to provide a clear, understandable explanation for the AI's loan decision directly violates the transparency principle. Microsoft's responsible AI principles require that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact. A deep neural network's complex, non-linear decision boundaries and lack of inherent interpretability make it a 'black box,' which undermines the required transparency.
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.
✗
Fairness
Why it's wrong here
Fairness is about avoiding bias and ensuring equitable outcomes; while important, the core issue here is explainability, not bias.
✓
Transparency
Why this is correct
Transparency ensures that AI systems are understandable and that decisions can be explained to users, which is directly missing in this case.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reliability & Safety
Why it's wrong here
Reliability & Safety ensures the system operates correctly and safely under various conditions, not specifically about providing explanations.
✗
Privacy & Security
Why it's wrong here
Privacy & Security protects personal data from unauthorized access or misuse; the issue here is about explainability, not data protection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'transparency' with 'fairness,' assuming that an unexplained decision must be biased, but the question specifically tests the principle of providing understandable explanations, not the presence of discrimination.
Detailed technical explanation
How to think about this question
Deep neural networks use multiple hidden layers with non-linear activation functions (e.g., ReLU, sigmoid) that learn hierarchical feature representations, making it nearly impossible to trace a specific output back to individual input features without specialized interpretability tools like LIME or SHAP. In practice, financial institutions often use simpler models (e.g., logistic regression with L1 regularization) or post-hoc explanation methods to comply with regulations like GDPR's 'right to explanation.' The opacity of deep learning models is a well-known trade-off between predictive accuracy and interpretability.
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: Transparency — The bank's inability to provide a clear, understandable explanation for the AI's loan decision directly violates the transparency principle. Microsoft's responsible AI principles require that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact. A deep neural network's complex, non-linear decision boundaries and lack of inherent interpretability make it a 'black box,' which undermines the required transparency.
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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