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

The verified answer is C. Faithfulness. The company is evaluating foundation models for a summarization task by using an LLM-as-a-judge evaluation job in Amazon Bedrock. AWS documentation states that with a model evaluation job that uses a judge model, Amazon Bedrock uses one LLM to score another model’s responses and provide an explanation of how it scored each prompt and response pair. AWS also states that Amazon Bedrock provides built-in metrics that can be selected for these judge-based evaluation jobs.

Among the answer choices, Faithfulness is the only valid built-in metric for an Amazon Bedrock LLM-as-a-judge model evaluation job. AWS API documentation lists the valid built-in metric names for LLM-as-a-judge evaluation jobs, including Correctness, Completeness, Faithfulness, Helpfulness, Coherence, Relevance, FollowingInstructions, ProfessionalStyleAndTone, and responsible AI metrics such as Harmfulness, Stereotyping, and Refusal. The same documentation identifies Summarization as a valid task type for model evaluation jobs. Therefore, Faithfulness is a valid built-in metric for evaluating whether generated summaries remain grounded in the source input rather than introducing unsupported claims.

Option A. Context relevance and option B. Context coverage are incorrect for this question because AWS lists those as metrics for knowledge base retrieval-only evaluation jobs, not general LLM-as-a-judge model evaluation of foundation model summarization output. These metrics evaluate retrieved context, not the quality of a summarization model’s generated response. Option D. RMSE is also incorrect because root mean square error is a regression metric. It is not a built-in LLM-as-a-judge metric for summarization in Amazon Bedrock.

Because the task is summarization and the evaluation job is LLM-as-a-judge, the correct built-in evaluation metric from the available choices is Faithfulness.

The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Transcribe:

" Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users. "

(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe)

Detailed Explanation:

Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.

Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.

Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.

Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.

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.

Chain-of-thought prompting is a prompt engineering technique where you instruct the model to explain its reasoning step by step, which is particularly useful for tasks involving logic, math, or reasoning.

A is correct: Asking the model to " explain its reasoning step by step " directly invokes chain-of-thought prompting, as documented in AWS and generative AI literature.

B is unrelated (prompt injection is a security concern).

C (few-shot) provides examples, but doesn’t specifically require step-by-step reasoning.

D (templating) is about structuring the prompt format.

" Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks. "

(Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)