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Guidelines for Responsible AIhardMultiple SelectObjective-mapped

AIF-C01 Guidelines for Responsible AI Practice Question

Which THREE considerations are important when implementing responsible AI for a production NLP system? (Choose three.)

⚠ Common exam trap

AWS often tests the distinction between general security practices (like encryption) and the specific pillars of responsible AI (fairness, transparency, accountability, and robustness), leading candidates to confuse data protection with ethical AI governance.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Continuously monitor model outputs for bias and drift

Continuous monitoring of model outputs for bias and drift is essential for maintaining responsible AI in production NLP systems. As language patterns and data distributions evolve over time, a model that was initially fair and accurate can develop harmful biases or performance degradation, so automated monitoring ensures ongoing alignment with ethical standards and regulatory requirements.

Answer analysis

Option-by-option breakdown

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

  • Obtain FDA approval for the model

    Why it's wrong here

    FDA approval is required only for medical devices, not all NLP systems.

  • Continuously monitor model outputs for bias and drift

    Why this is correct

    Production models require ongoing monitoring to ensure fairness over time.

  • Apply encryption at rest for all training code

    Why it's wrong here

    Encryption is a security measure, not specific to responsible AI.

  • Publish model cards detailing intended use, performance, and limitations

    Why this is correct

    Model cards promote transparency and accountability.

  • Include bias detection in the CI/CD pipeline for every model update

    Why this is correct

    Bias checks should be automated as part of model validation.

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Last reviewed: Jul 4, 2026

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