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

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.

Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.

Example-Driven Prompts:

This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.

It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.

Why Option A is Correct:

Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.

Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.

Why Other Options are Incorrect:

B. Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.

C. New text passage without context: Provides no guidance or learning context for the model.

D. Unrelated task examples: Would confuse the model and lead to inaccurate results.

The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).

B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.

A and C require manual effort, increasing operational overhead.

D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.

"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity."

(Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)

The correct answer is AWS Artifact, which is a central resource for accessing AWS compliance documentation, audit reports, and certifications from both AWS and third-party providers, including ISVs. According to AWS documentation, Artifact supports notifications for newly available reports. Customers can subscribe to notification emails when specific compliance documents or updates are published. This feature is ideal for regulated industries or companies needing third-party verification of compliance and security postures. Artifact provides access to reports such as SOC 2, ISO, PCI, and more, and is commonly used for audit preparation and vendor risk management. In contrast, AWS Audit Manager helps create internal audit frameworks but is not focused on third-party report distribution. AWS Data Exchange is for marketplace data sharing, and Trusted Advisor provides performance and cost optimization recommendations, not compliance document alerts. Thus, AWS Artifact is the purpose-built service that meets this requirement.

Referenced AWS AI/ML Documents and Study Guides:

AWS Artifact Documentation – Compliance Reports and Subscriptions

AWS Security and Compliance Whitepaper – Using Artifact for Third-Party Assurance