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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AWS AI Services Overview26%- Describe AWS AI services for specific use cases
  • 1. Explain AWS HealthScribe and other domain-specific services
  • 2. Describe Amazon Polly, Rekognition, Transcribe, and Translate
  • 3. Identify services for NLP, computer vision, and recommendations
- Describe Amazon Bedrock capabilities
  • 1. Identify Bedrock features (agents, knowledge bases, guardrails)
  • 2. Describe model invocation, prompts, and responses
  • 3. Explain security, privacy, and compliance features
  • 4. Explain foundation models (FMs) available in Bedrock
- 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 SageMaker capabilities
  • 1. Recognize MLOps capabilities in SageMaker
  • 2. Describe built-in algorithms and SageMaker JumpStart
  • 3. Explain model training and deployment options
  • 4. Identify SageMaker features for ML workflows
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. Explain training, inference, and fine-tuning
  • 2. Define models, algorithms, and parameters
  • 3. Describe underfitting and overfitting
  • 4. Define common ML metrics (accuracy, precision, recall, F1 score)
- Explain the fundamental concepts of AI and ML
  • 1. Identify common use cases for AI/ML
  • 2. Explain supervised and unsupervised learning
  • 3. Define generative AI and its key concepts
  • 4. Differentiate between AI, ML, and deep learning
Topic 3: Responsible AI20%- Implement responsible AI best practices
  • 1. Apply human oversight in AI decision-making
  • 2. Evaluate AI outputs for quality and safety
  • 3. Implement appropriate guardrails for AI applications
- 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. Describe strategies for bias mitigation
  • 2. Define fairness, transparency, and privacy in AI systems
  • 3. Identify potential biases in AI/ML models
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. Explain function calling and tool use in AI applications
  • 4. Describe prompt engineering techniques
- Implement AI applications using AWS services
  • 1. Implement prompt templates and chain-of-thought reasoning
  • 2. Use knowledge bases for context-aware responses
  • 3. Configure model parameters (temperature, top-p, top-k)
  • 4. Build applications using Amazon Bedrock APIs and SDKs
- Evaluate and optimize AI applications
  • 1. Identify performance bottlenecks and optimization strategies
  • 2. Assess application outputs for relevance and accuracy
  • 3. Implement caching and cost optimization techniques
  • 4. Apply evaluation frameworks for AI applications

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Amazon AWS Certified AI Practitioner Sample Questions (Q122-Q127):

NEW QUESTION # 122
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model

Answer:

Explanation:

Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.


NEW QUESTION # 123
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:

Explanation:


NEW QUESTION # 124
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

Answer: D


NEW QUESTION # 125
Which functionality does Amazon SageMaker Clarify provide?

Answer: C

Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.


NEW QUESTION # 126
A company is building an application. The application will use a large language model (LLM) to generate citations for documents and check for plagiarized content.
Which risk is the company trying to mitigate by using this application?

Answer: C

Explanation:
Intellectual property infringement occurs when protected content, including copyrighted materials, is used or reproduced without appropriate authorization.
Generative AI applications can introduce intellectual property risks when generated content reproduces protected material without proper attribution or necessary permissions.
By generating citations and detecting potentially plagiarized text, the application helps identify source material and supports responsible content usage.
Document attribution can improve transparency and traceability, while plagiarism analysis can highlight similarities between generated documents and existing publications.
Response manipulation refers to unauthorized influence over model behavior. Response bias involves unfair or systematically distorted results. Misinformation concerns inaccurate or misleading information.
These risks are distinct from the copyright and attribution concerns described in the question.
Importantly, citations and plagiarism checks reduce intellectual property risks but do not automatically establish legal permission or guarantee copyright compliance.


NEW QUESTION # 127
......

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