Amazon Web Services Updated AIF-C01 Exam Questions and Answers by hubert

The correct answer is A, as the most effective way to ensure fairness in AI systems is to audit and diversify training data. AWS Responsible AI documentation stresses that bias originates primarily from unbalanced or incomplete datasets. By including samples from all demographic groups, the model learns patterns that generalize fairly across populations. AWS SageMaker Clarify supports data bias detection and offers fairness metrics like demographic parity and equal opportunity difference. Deeper models (option B) or secrecy (option C) do not mitigate bias and may worsen transparency . Static test datasets (option D) fail to capture evolving data distributions. Therefore, ongoing bias reviews and diverse datasets form the foundation of responsible generative AI for financial use cases.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Whitepaper – Fairness and Bias Mitigation

Amazon SageMaker Clarify Documentation – Bias Detection

With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).

Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.

Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.

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AWS Shared Responsibility Model

AWS documentation defines prompt injection as a security and safety risk in which a user crafts input designed to override, manipulate, or bypass system-level instructions, safeguards, or intended model behavior. The example provided is a classic prompt injection attempt, where the user explicitly instructs the AI assistant to ignore prior rules and operate without restrictions.

In this scenario, the attacker is not exploiting training data or causing factual errors, but rather attempting to change the control flow and behavior of the AI system through malicious or manipulative prompts. AWS identifies prompt injection as a critical risk for generative AI systems, especially those exposed to end users through chat interfaces, APIs, or customer-facing applications.

The other options do not apply. Data bias relates to skewed or unfair training data. Hallucination refers to generating incorrect or fabricated information. Data exposure involves leaking sensitive or private data. None of these describe an attempt to override system instructions.

AWS recommends multiple mitigation strategies for prompt injection risks, including instruction hierarchy enforcement, prompt isolation, input validation, output filtering, and grounding responses using techniques such as Retrieval Augmented Generation. AWS also emphasizes the importance of clearly separating system instructions from user inputs to prevent unauthorized behavior changes.

Prompt injection is categorized by AWS as part of Responsible AI and security governance, highlighting the need for robust guardrails when deploying AI assistants in production. Therefore, the correct answer is prompt injection.

When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.

Option C (Correct): " Increase the volume of data that is used in training " : Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.

Option A: " Reduce the volume of data that is used in training " is incorrect, as reducing data volume would likely worsen the overfitting problem.

Option B: " Add hyperparameters to the model " is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.

Option D: " Increase the model training time " is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.

AWS AI Practitioner References:

Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model ' s generalization capability and reduce the risk of overfitting.