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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q361-Q366):

NEW QUESTION # 361
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?

Answer: A


NEW QUESTION # 362
How can companies use large language models (LLMs) securely on Amazon Bedrock?

Answer: D


NEW QUESTION # 363
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning

Answer:

Explanation:


NEW QUESTION # 364
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?

Answer: A

Explanation:
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
* A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview)
* B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
* C (CloudWatch) is for monitoring, not ML training or deployment.
* D (Rekognition) is for image/video analysis.


NEW QUESTION # 365
A company is using Amazon SageMaker AI to develop AI/ML solutions. The company must use only approved data for model training. The AI/ML solutions must comply with company policy and ethical guidelines.
Which solution will meet these requirements?

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon SageMaker Model Cards provide a structured way to document:
* Approved training datasets
* Intended use cases
* Ethical considerations and limitations
* Compliance with internal policies and governance standards
Model Cards are a Responsible AI governance tool, enabling transparency and accountability throughout the model lifecycle.
Why the other options are incorrect:
* Catalog (A) organizes ML assets but does not enforce ethical or policy documentation.
* Clarify (B) detects bias and explainability issues but does not govern approved data usage.
* Model Registry (C) manages model versions and approvals but does not document ethical intent.
AWS AI document references:
* Amazon SageMaker Model Cards Documentation
* AWS Responsible AI Practices
* AI Governance on AWS


NEW QUESTION # 366
......

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