AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  AI systems should run as fast as possible regardless of accuracy

  Why it's wrong here

  Optimizing for raw speed regardless of accuracy is a performance trade-off, not a fairness consideration. Fairness focuses on how model benefits and harms are distributed across different demographic segments, while speed only measures computational efficiency or latency. A very fast model can still be unfair if it makes inaccurate predictions for certain groups, and prioritizing speed may even exacerbate bias if it leads to oversimplified features. Therefore, speed itself neither contributes to nor prevents equitable treatment.

• ✓

  AI systems should not perpetuate or amplify societal biases against specific groups

  Why this is correct

  Fair AI must not perpetuate or amplify societal biases against specific groups, meaning the model should not systematically disadvantage people based on race, gender, age, religion, or other protected characteristics. This requires scrutinizing training data for historical biases, evaluating model outputs with fairness metrics such as demographic parity or equalized odds, and mitigating harms when disparities are found. Without this, AI can scale existing inequities by encoding them into automated decisions. This principle is a core pillar of Microsoft's responsible AI framework.

• ✗

  AI systems should be available 24/7 without any downtime

  Why it's wrong here

  24/7 availability is a resilience and uptime property, not a fairness property. Fairness in AI requires that model decisions do not discriminate against or disadvantage individuals based on protected attributes like race, gender, or age. An always-on system could still be deeply unfair if its training data or algorithms embed bias. Thus, continuous operation does not address whether outcomes are equitable across demographic groups.

• ✗

  AI systems should always produce the same output for the same input

  Why it's wrong here

  Producing the same output for the same input is a consistency or reproducibility requirement, which is conceptually orthogonal to fairness. Fairness is about avoiding unequal treatment across groups; it permits different outputs for different inputs when those differences are justified by legitimate factors. Determinism alone can lock in biased patterns, and even a deterministic model may systematically favor one group over another. Therefore, identical input-output mapping does not guarantee equitable treatment.

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