Exactprep AIF-C01 Questions

Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text—like customer complaints—is a classic application of generative AI.

A is correct:

" Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text. "

(Reference: AWS Generative AI Use Cases)

B and C are standard ML classification/segmentation tasks.

D is a regression/prediction task, not generative.

This scenario demonstrates intellectual property (IP) infringement, where an AI-generated artifact (a cloned voice) uses the identifiable traits of a person without permission. According to AWS Responsible AI practices, using a person’s likeness, image, or voice in generative AI systems requires explicit rights and consent. Foundation models trained or applied without respecting these ownership boundaries violate ethical and legal frameworks under IP law. The AWS documentation emphasizes that generative AI must respect copyrighted or trademarked works and human likenesses. Responsible deployment requires consent-based data usage, license tracking, and content watermarking to prevent misappropriation. AWS Bedrock enforces such compliance principles through Guardrails and policy-based moderation to ensure content generation aligns with ethical and lawful standards.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Guidelines – Intellectual Property and Ethical Use

Amazon Bedrock Documentation – Responsible Use of Generative AI

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens: Would increase costs, not lower them.

D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

Amazon SageMaker is AWS’s fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.

A is correct:

" Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy. "

(Reference: Amazon SageMaker Overview)

B (Amazon Personalize) is for recommendations, not general ML or waste reduction.

C (CloudWatch) is for monitoring, not ML training or deployment.

D (Rekognition) is for image/video analysis.