Practice Free AIF-C01 Exam Online Questions – Page 8

Question #71

A software company has deployed an AI model to translate paragraphs of text into a user’s chosen language. The model can produce a confidence score for the translations. The company wants to incorporate its employees into a review process to validate and improve the model’s translations.

Which AWS solution will meet these requirements?

A . Amazon SageMaker Clarify
B . Amazon Augmented AI (Amazon A2I)
C . Amazon SageMaker Model Monitor
D . Amazon Bedrock Agents

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Question #72

A company is building an application that needs to generate synthetic data that is based on existing data.

Which type of model can the company use to meet this requirement?

A . Generative adversarial network (GAN)
B . XGBoost
C . Residual neural network
D . WaveNet

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Question #73

A company wants to enhance response quality for a large language model (LLM) for complex problem-solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.

Which prompt engineering technique meets these requirements?

A . Few-shot prompting
B . Zero-shot prompting
C . Directional stimulus prompting
D . Chain-of-thought prompting

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Correct Answer: D
D

Explanation:

The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning."

(Source: AWS Bedrock User Guide, Prompt Engineering Techniques)

Detailed

Option A: Few-shot prompting Few-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.

Option B: Zero-shot prompting Zero-shot prompting relies on the LLM’s pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.

Option C: Directional stimulus prompting Directional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.

Option D: Chain-of-thought prompting This is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.

Reference: AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html)

AWS AI Practitioner Learning Path: Module on Generative AI Prompting

Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)

Below are the corrected and formatted questions based on the provided input, following the specified format. Each question is aligned with the main topics from the AWS AI Practitioner certification, and answers are provided with comprehensive explanations referencing official AWS

documentation or study guides. Since the exact AWS AI Practitioner documents are not publicly available in full, I will rely on authoritative AWS documentation, whitepapers, and blogs available as of May 17, 2025, to ensure accuracy. If specific document excerpts are unavailable, I will use the most relevant AWS resources and clearly note the references.

Question #74

What does an F1 score measure in the context of foundation model (FM) performance?

A . Model precision and recall
B . Model speed in generating responses
C . Financial cost of operating the model
D . Energy efficiency of the model’s computations

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Question #75

A design company is using a foundation model (FM) on Amazon Bedrock to generate images for various projects. The company wants to have control over how detailed or abstract each generated image appears.

Which model parameter should the company modify?

A . Model checkpoint
B . Batch size
C . Generation step
D . Token length

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Question #76

A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.

Which solution meets these requirements?

A . Track the model changes by using Git.
B . Track the model changes by using Amazon Fraud Detector.
C . Track the model changes by using Amazon SageMaker Model Cards.
D . Track the model changes by using Amazon Comprehend.

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Question #77

A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.

A . Amazon Q Developer
B . Amazon Polly
C . Amazon Rekognition
D . AWS HealthScribe

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Question #78

In which stage of the generative AI model lifecycle are tests performed to examine the model’s accuracy?

A . Deployment
B . Data selection
C . Fine-tuning
D . Evaluation

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Question #79

A company wants to use its documents as a knowledge base for a large language model (LLM) in a Retrieval Augmented Generation (RAG) solution.

Which solution will meet these requirements?

A . Encrypt each document with encryption keys.
B . Create embeddings from document chunks.
C . Label the document data with metadata.
D . Generate one-hot encoding for each document.

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Question #80

A company stores customer personally identifiable information (PII) data. The company must store the PII data within the company’s AWS Region.

Which aspect of governance does this describe?

A . Data mining
B . Data residency
C . Pre-training bias
D . Geolocation routing

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