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 healthcare research organization publishes an AI system that diagnoses skin conditions from images. In a study, they discover that the model's accuracy is significantly lower for people with darker skin tones compared to those with lighter skin tones. According to Microsoft's Responsible AI principles, which principle most directly requires the organization to disclose this limitation in their documentation?
Why wrong: Fairness is about reducing bias and ensuring equitable outcomes, but it does not directly require disclosure of limitations.
B
Transparency
Transparency requires AI systems to be open about their limitations, such as performance disparities across different groups.
C
Accountability
Why wrong: Accountability ensures that organizations take responsibility for their AI systems, but it does not specifically mandate disclosure of performance limitations.
D
Privacy and Security
Why wrong: Privacy and Security are concerned with protecting data and user information, not with disclosing model limitations.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transparency
The Transparency principle requires AI systems to be understandable and for their limitations to be clearly communicated. In this scenario, the organization must disclose the model's lower accuracy for darker skin tones because users and clinicians need to know when the system is less reliable to make informed decisions. Without this disclosure, the system could be misused or trusted inappropriately, violating the core tenet of 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 reducing bias and ensuring equitable outcomes, but it does not directly require disclosure of limitations.
✓
Transparency
Why this is correct
Transparency requires AI systems to be open about their limitations, such as performance disparities across different groups.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accountability
Why it's wrong here
Accountability ensures that organizations take responsibility for their AI systems, but it does not specifically mandate disclosure of performance limitations.
✗
Privacy and Security
Why it's wrong here
Privacy and Security are concerned with protecting data and user information, not with disclosing model limitations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the principle of Fairness (which addresses the bias itself) with Transparency (which requires disclosure of the bias), leading them to select Fairness when the question specifically asks about disclosing the limitation in documentation.
Detailed technical explanation
How to think about this question
Under the hood, transparency in AI systems often involves providing model cards or datasheets that document performance metrics across different subgroups, such as accuracy stratified by skin tone using the Fitzpatrick scale. In real-world scenarios, a dermatology AI without such disclosures could lead to misdiagnosis in clinical settings, as clinicians might over-rely on the system without knowing its degraded performance on certain populations. This principle aligns with regulatory expectations like the EU AI Act's requirement for transparency in high-risk 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 Transparency principle requires AI systems to be understandable and for their limitations to be clearly communicated. In this scenario, the organization must disclose the model's lower accuracy for darker skin tones because users and clinicians need to know when the system is less reliable to make informed decisions. Without this disclosure, the system could be misused or trusted inappropriately, violating the core tenet of 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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