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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q329-Q334):

NEW QUESTION # 329
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.
Which solution meets these requirements?

Answer: C


NEW QUESTION # 330
What does inference refer to in the context of AI?

Answer: D

Explanation:
* Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.
* A is algorithm research, C refers to ensemble learning, D is data collection.
# Reference:
AWS ML Glossary - Inference


NEW QUESTION # 331
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.
Which option represents the model's problem?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
Interpretability refers to understanding how a model arrives at its predictions.
Nondeterminism refers to variation in outputs across runs due to probabilistic sampling.
Accuracy is a general performance metric, not the specific phenomenon described.
AWS AI Study Guide Reference:
AWS generative AI challenges and limitations
AWS guidance on hallucinations in foundation models


NEW QUESTION # 332
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?

Answer: C

Explanation:
Comprehensive and Detailed
Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
Temperature (A) and Top P (C) control randomness, not input size.
Fine-tuning (B) is irrelevant to input truncation failures.
Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)


NEW QUESTION # 333
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.
Which option represents the model's problem?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
* Interpretability refers to understanding how a model arrives at its predictions.
* Nondeterminism refers to variation in outputs across runs due to probabilistic sampling.
* Accuracy is a general performance metric, not the specific phenomenon described.
AWS AI Study Guide References:
* AWS generative AI challenges and limitations
* AWS guidance on hallucinations in foundation models


NEW QUESTION # 334
......

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