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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Responsible AI20%- Implement responsible AI best practices
  • 1. Evaluate AI outputs for quality and safety
  • 2. Implement appropriate guardrails for AI applications
  • 3. Apply human oversight in AI decision-making
- Understand governance and compliance requirements
  • 1. Describe model interpretability and explainability
  • 2. Recognize regulatory and ethical considerations
  • 3. Explain data privacy regulations affecting AI
- Explain the principles of responsible AI
  • 1. Define fairness, transparency, and privacy in AI systems
  • 2. Describe strategies for bias mitigation
  • 3. Identify potential biases in AI/ML models
Topic 2: AWS AI Services Overview26%- Identify appropriate AWS AI services for given scenarios
  • 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
  • 2. Compare AWS AI services by capability and use case
  • 3. Determine when to use pre-trained models vs. custom models
- Describe Amazon Bedrock capabilities
  • 1. Explain security, privacy, and compliance features
  • 2. Describe model invocation, prompts, and responses
  • 3. Explain foundation models (FMs) available in Bedrock
  • 4. Identify Bedrock features (agents, knowledge bases, guardrails)
- Describe Amazon SageMaker capabilities
  • 1. Describe built-in algorithms and SageMaker JumpStart
  • 2. Recognize MLOps capabilities in SageMaker
  • 3. Identify SageMaker features for ML workflows
  • 4. Explain model training and deployment options
- Describe AWS AI services for specific use cases
  • 1. Identify services for NLP, computer vision, and recommendations
  • 2. Explain AWS HealthScribe and other domain-specific services
  • 3. Describe Amazon Polly, Rekognition, Transcribe, and Translate
Topic 3: AI/ML Fundamentals24%- Identify foundational terminology and definitions
  • 1. Explain training, inference, and fine-tuning
  • 2. Define models, algorithms, and parameters
  • 3. Define common ML metrics (accuracy, precision, recall, F1 score)
  • 4. Describe underfitting and overfitting
- Explain the fundamental concepts of AI and ML
  • 1. Identify common use cases for AI/ML
  • 2. Define generative AI and its key concepts
  • 3. Differentiate between AI, ML, and deep learning
  • 4. Explain supervised and unsupervised learning
- Understand the AI/ML lifecycle
  • 1. Identify phases of the ML lifecycle
  • 2. Describe model evaluation and deployment considerations
  • 3. Recognize data collection, preprocessing, and feature engineering stages
Topic 4: AI Application Development30%- Understand foundational concepts for building AI applications
  • 1. Explain prompts, tokens, and context windows
  • 2. Describe prompt engineering techniques
  • 3. Explain function calling and tool use in AI applications
  • 4. Identify retrieval-augmented generation (RAG) concepts
- Evaluate and optimize AI applications
  • 1. Apply evaluation frameworks for AI applications
  • 2. Identify performance bottlenecks and optimization strategies
  • 3. Assess application outputs for relevance and accuracy
  • 4. Implement caching and cost optimization techniques
- Implement AI applications using AWS services
  • 1. Build applications using Amazon Bedrock APIs and SDKs
  • 2. Configure model parameters (temperature, top-p, top-k)
  • 3. Implement prompt templates and chain-of-thought reasoning
  • 4. Use knowledge bases for context-aware responses

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Amazon AWS Certified AI Practitioner Sample Questions (Q264-Q269):

NEW QUESTION # 264
A company wants to fine-tune a foundation model (FM) for a specific use case. The company needs to deploy the FM on Amazon Bedrock for internal use.
Which solution will meet these requirements?

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock supports importing custom foundation models that have been trained or fine-tuned outside of Bedrock, including models customized using Amazon SageMaker AI.
Amazon SageMaker AI provides:
* Full control over model training and fine-tuning
* Ability to train models using approved internal datasets
* Advanced customization beyond prompt-based techniques
After customization in SageMaker, the trained model can be imported into Amazon Bedrock for managed, scalable inference and internal deployment.
Why the other options are incorrect:
* A (Guardrails) enforce safety, compliance, and output controls; they do not create or fine-tune models.
* B (Amazon Personalize) is a recommendation service, not a foundation model customization tool.
* C (Agents) orchestrate tasks and tool usage but do not modify or fine-tune model weights.
AWS AI document references:
* Amazon Bedrock Documentation - section on Custom model import
* Amazon SageMaker AI Overview - section on model training and fine-tuning
* Foundation Models on AWS - customization approaches


NEW QUESTION # 265
A company is exploring Amazon Nova models in Amazon Bedrock. The company needs a multimodal model that supports multiple languages.

Answer: C

Explanation:
* Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.
* Nova Lite is optimized for lightweight, faster inference at lower cost.
* Nova Canvas is a creative tool for visual design.
* Nova Reel is optimized for video-related use cases.
# Reference:
AWS Documentation - Amazon Nova Models


NEW QUESTION # 266
A company needs to scan its Amazon EC2-based ML infrastructure for security vulnerabilities before deploying generative AI (GenAI) models.
Which AWS service provides automated vulnerability assessment?

Answer: A

Explanation:
The verified answer is C. Amazon Inspector. The company needs automated vulnerability assessment for Amazon EC2-based infrastructure. AWS documentation states that Amazon Inspector scans EC2 instances for package vulnerabilities and network reachability issues and produces findings. It compares instance metadata against vulnerability intelligence and security advisory rules. This is exactly the service used to assess EC2 infrastructure before deploying sensitive workloads such as generative AI models.
AWS also documents that Amazon Inspector scans EC2 instances for common vulnerabilities and exposures, operating system package vulnerabilities, programming language package vulnerabilities, network exposure issues, and network reachability issues. This confirms that Inspector is the correct service for automated vulnerability assessment of EC2-based ML infrastructure.
AWS CloudFormation is incorrect because it provisions infrastructure as code. It can create EC2 instances, security groups, and related resources, but it does not perform vulnerability assessment. Amazon Comprehend is incorrect because it is a natural language processing service used to extract insights from text. It does not scan infrastructure. AWS Trusted Advisor is incorrect because it provides best-practice recommendations across categories such as cost optimization, security, performance, and service limits, but it is not the dedicated automated vulnerability assessment service for EC2 instances.


NEW QUESTION # 267
A company stores its AI datasets in Amazon S3 buckets. The company wants to share the S3 buckets with its business partners. The company needs to avoid accidentally sharing sensitive data.
Which AWS service should the company use to discover sensitive data in the dataset?

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Macie uses machine learning to:
* Discover sensitive data such as PII
* Classify data stored in Amazon S3
* Help prevent unintended data exposure
AWS security guidance recommends Macie before data sharing to ensure compliance and privacy protection.
Why the other options are incorrect:
* Kendra (A) is a search service.
* Textract (C) extracts text from documents.
* Data Exchange (D) shares datasets, not analyzes sensitivity.
AWS AI document references:
* Amazon Macie Overview
* Protecting Sensitive Data in S3
* Data Privacy and Governance on AWS


NEW QUESTION # 268
An AI practitioner has trained a model on a training dataset. The model performs well on the training data.
However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?

Answer: A

Explanation:
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
Overfitting: The model memorizes the training data patterns instead of generalizing.
Underfitting (A) means the model performs poorly on both training and test data.
Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
Prompt engineering (B) applies to generative AI, not general ML training models.
# Reference:
AWS ML Glossary - Overfitting and Underfitting


NEW QUESTION # 269
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