The answer is accountability. Removing human oversight from a fully automated diagnostic system directly violates Microsoft’s responsible AI principle of accountability, which mandates that humans must remain responsible for AI-driven decisions, especially in high-stakes healthcare scenarios where errors can have life-or-death consequences. Even with a claimed 99% accuracy, eliminating any human doctor review means no one can intervene, validate, or take ownership of the system’s outputs, breaking the chain of responsibility. On the AI-900 exam, this question tests your understanding of how accountability applies to critical decision-making, often appearing as a trap where test-takers confuse it with reliability or fairness—remember that accountability is about human oversight and ownership, not just system performance. A useful memory tip: “Accountability = A human is Accountable for the AI’s actions,” so if no human is in the loop, accountability is the principle most directly violated.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 start-up proposes a fully automated AI system to diagnose patients from medical scans without any human doctor review. They claim the system is 99% accurate. According to Microsoft's responsible AI principles, which principle is most directly violated by removing human oversight from this critical decision-making process?
Why wrong: Fairness is about ensuring the system doesn't discriminate; while important, the core issue here is lack of human oversight.
B
Reliability and safety
Why wrong: Reliability is about performing correctly; the system may still be reliable, but the principle of accountability emphasizes human oversight for high-stakes decisions.
C
Transparency
Why wrong: Transparency requires explainability, but the primary concern is the absence of humans in the loop.
D
Accountability
Accountability demands that AI systems are designed with appropriate human oversight to ensure responsible use and to handle edge cases. Fully automating diagnosis removes human accountability.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Accountability
Option D is correct because removing human oversight from a fully automated diagnostic system violates the accountability principle. Microsoft's responsible AI principle of accountability requires that humans remain responsible for AI-driven decisions, especially in high-stakes healthcare scenarios where errors can have life-or-death consequences. By eliminating any human doctor review, the start-up fails to ensure that a human can intervene, validate, or take responsibility for the system's outputs.
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 ensuring the system doesn't discriminate; while important, the core issue here is lack of human oversight.
✗
Reliability and safety
Why it's wrong here
Reliability is about performing correctly; the system may still be reliable, but the principle of accountability emphasizes human oversight for high-stakes decisions.
✗
Transparency
Why it's wrong here
Transparency requires explainability, but the primary concern is the absence of humans in the loop.
✓
Accountability
Why this is correct
Accountability demands that AI systems are designed with appropriate human oversight to ensure responsible use and to handle edge cases. Fully automating diagnosis removes human accountability.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse accountability with transparency or reliability, assuming that a highly accurate system is inherently trustworthy, but Microsoft's principles explicitly require human responsibility for outcomes, not just system performance.
Detailed technical explanation
How to think about this question
Under the hood, Microsoft's responsible AI framework operationalizes accountability through human-in-the-loop (HITL) design patterns, where a human reviewer must validate critical decisions, especially in clinical decision support systems. In real-world healthcare AI deployments, such as those using Azure Health Bot or Medical Imaging AI, the system provides a confidence score and highlights regions of interest, but a radiologist always makes the final diagnosis to comply with regulatory standards like HIPAA and FDA guidance on software as a medical device (SaMD).
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: Accountability — Option D is correct because removing human oversight from a fully automated diagnostic system violates the accountability principle. Microsoft's responsible AI principle of accountability requires that humans remain responsible for AI-driven decisions, especially in high-stakes healthcare scenarios where errors can have life-or-death consequences. By eliminating any human doctor review, the start-up fails to ensure that a human can intervene, validate, or take responsibility for the system's outputs.
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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