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Amazon AIF-C01 Exam Questions
Free Amazon AIF-C01 Exam Actual Questions
Note: Premium Questions for AIF-C01 were last updated On May. 22, 2026 (see below)
A media streaming platform wants to provide movie recommendations to users based on the users' account history.
Amazon Personalize is a fully managed ML service for personalized recommendations (movies, products, music, etc.) based on user behavior and history.
Polly converts text to lifelike speech.
Comprehend performs NLP tasks like sentiment analysis.
Transcribe is speech-to-text.
Reference:
AWS Documentation -- Amazon Personalize
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.
AWS generative AI guidance explains that:
Prompts can define tone, voice, and response structure
Iterative refinement ensures consistent outputs
Prompt refinement requires no model retraining
Why the other options are incorrect:
Token limits (A) affect length, not tone.
Batch inferencing (B) affects processing mode, not response style.
Higher temperature (D) increases randomness, reducing consistency.
AWS AI document references:
Prompt Engineering Best Practices
Controlling Model Output Tone
A company is using AI to build a toy recommendation website that suggests toys based on a customer's interests and age. The company notices that the AI tends to suggest stereotypically gendered toys.
Which AWS service or feature should the company use to investigate the bias?
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon SageMaker Clarify is designed to detect and explain bias in ML models and datasets.
AWS Responsible AI guidance recommends Clarify to:
Identify bias in predictions
Analyze feature attribution
Support fairness and ethical AI practices
Why the other options are incorrect:
Rekognition (A) analyzes images, not recommendation bias.
Amazon Q Developer (B) assists developers with code.
Comprehend (C) performs NLP tasks, not bias analysis.
AWS AI document references:
Amazon SageMaker Clarify Documentation
Detecting Bias in AI Systems
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?
Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
'Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease.'
(Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock)
Detailed
Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.
Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.
Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.
Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.
AWS Bedrock User Guide: Introduction to Amazon Bedrock (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Generative AI Services
AWS Documentation: Generative AI on AWS (https://aws.amazon.com/generative-ai/)
What is the benefit of fine-tuning a foundation model (FM)?
Comprehensive and Detailed Explanation from AWS AI Documents:
Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain-specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).
From AWS Docs:
''With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases.''
''Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain.''
Reference:
AWS Documentation -- Fine-tuning foundation models in Amazon Bedrock
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