AIF-C01

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The dumps for AIF-C01 exam was last updated on Jul 15,2025 .

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Question#1

A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics.
Which ML strategy should the company use to meet these requirements?

A. Unsupervised learning

B. Supervised learning

C. Reinforcement learning

D. Semi-supervised learning

Explanation:
The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement. Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Unsupervised learning is used to identify patterns or groupings in data without labeled outcomes. Common applications include clustering, such as grouping similar customers or products based on their characteristics, using algorithms like K-means or hierarchical clustering." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)
Detailed
Option A: Unsupervised learning This is the correct answer. Unsupervised learning, specifically clustering, is designed to group similar entities (e.g., customers or products) based on their characteristics without requiring labeled data.
Option B: Supervised learning Supervised learning requires labeled data to train a model for prediction or classification, which is not applicable here since the task involves grouping without predefined labels.
Option C: Reinforcement learning Reinforcement learning involves training an agent to make decisions through rewards and penalties, not for grouping data. This option is irrelevant.
Option D: Semi-supervised learning Semi-supervised learning uses a mix of labeled and unlabeled data, but the task here does not involve any labeled data, making unsupervised learning more appropriate.
Reference: AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Unsupervised Learning Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Unsupervised Learning (https://aws.amazon.com/machine-learning/)

Question#2

A company wants to build and deploy ML models on AWS without writing any code.
Which AWS service or feature meets these requirements?

A. Amazon SageMaker Canvas

B. Amazon Rekognition

C. AWS DeepRacer

D. Amazon Comprehend

Question#3

Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

A. One-shot prompting

B. Prompt chaining

C. Tree of thoughts

D. Retrieval Augmented Generation (RAG)

Explanation:
Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step."
(Source: AWS Bedrock User Guide, Prompt Engineering Techniques)
Detailed
Option A: One-shot prompting One-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.
Option B: Prompt chaining This is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.
Option C: Tree of thoughts Tree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.
Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.
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/)

Question#4

A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?

A. Implement moderation APIs.

B. Retrain the model with a general public dataset.

C. Perform model validation.

D. Automate user feedback integration.

Explanation:
Moderation APIs, such as Amazon Rekognition’s Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.
Option A (Correct): "Implement moderation APIs": This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.
Option B: "Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.
Option C: "Perform model validation" is incorrect as it ensures model correctness, not content moderation.
Option D: "Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.
AWS AI Practitioner
Reference: AWS Content Moderation Services: AWS provides moderation APIs for filtering unwanted content from applications.

Question#5

An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?

A. Amazon Bedrock Guardrails

B. Amazon Bedrock Agents

C. Amazon Bedrock inference APIs

D. Amazon Bedrock custom models

Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs."
(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)
Detailed
Option A: Amazon Bedrock Guardrails This is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
Option B: Amazon Bedrock Agents Amazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
Option C: Amazon Bedrock inference APIs Amazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
Option D: Amazon Bedrock custom models Custom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
Reference: AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety
Amazon Bedrock Developer Guide: Building Safe AI Applications (https://aws.amazon.com/bedrock/)

Exam Code: AIF-C01 Q & A: 180 Q&As Updated: Jul 15,2025

Full AIF-C01 Exam Dumps Here

Exam Code: AIF-C01
Q & A: 180 Q&As
Updated: Jul 15,2025

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