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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q150-Q155):

NEW QUESTION # 150
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: C

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 # 151
Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?

Answer: D


NEW QUESTION # 152
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?

Answer: A


NEW QUESTION # 153
A company uses foundation models (FMs) to automate daily tasks. An AI practitioner is creating system instructions that include context relevant to the tasks. The AI practitioner wants to save and reuse the instructions in daily interactions with FMs in Amazon Bedrock.
Which Amazon Bedrock solution will meet these requirements?

Answer: B

Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Amazon Bedrock Prompt Management is designed to allow practitioners to create, version, store, and reuse prompts and system instructions when working with foundation models. It enables consistent reuse of contextual instructions across repeated interactions and applications.
Why the other options are incorrect:
* Knowledge Bases store and retrieve external data, not reusable system instructions.
* Guardrails enforce safety and policy controls, not prompt reuse.
* Playgrounds are for experimentation and testing, not long-term prompt storage.
AWS AI Study Guide References:
* Amazon Bedrock prompt management capabilities
* AWS best practices for prompt engineering and reuse


NEW QUESTION # 154
An online learning company with large volumes of education materials wants to use enterprise search.

Answer: D

Explanation:
The correct answer is C - Amazon Kendra, AWS's enterprise search service designed for organizations with large, diverse document repositories. Kendra uses machine learning and natural language understanding (NLU) to provide semantic search, meaning it retrieves results based on meaning rather than keyword matching. According to AWS documentation, Kendra is ideal for educational, enterprise, and knowledge- management scenarios where users need fast, accurate retrieval across PDFs, HTML, Office documents, FAQs, and multimedia transcripts. Kendra connectors can index content from S3, SharePoint, LMS platforms, and internal databases, making it perfect for large volumes of training materials. Amazon Comprehend (A) is for NLP tasks like entity extraction, not enterprise search. Amazon Textract (B) extracts text from PDFs and scanned materials but does not provide search capabilities. Amazon Personalize (D) is for personalized recommendations, not document retrieval. Kendra is purpose-built for enterprise search and aligns directly with the company's needs.
Referenced AWS Documentation:
* Amazon Kendra Developer Guide - Enterprise Search
* AWS ML Specialty Guide - Intelligent Search Systems


NEW QUESTION # 155
......

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