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

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Amazon AWS Certified AI Practitioner Sample Questions (Q128-Q133):

NEW QUESTION # 128
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Select TWO.)

Answer: C,E

Explanation:
* AWS CloudTrail: Logs all API calls to Amazon Bedrock.
* Amazon S3 Intelligent-Tiering: Optimizes storage costs for long-term retention with automatic tiering.
According to Amazon Bedrock Logging Documentation:
"CloudTrail records API activity and events, and logs can be stored in S3. For cost optimization, use S3 Intelligent-Tiering to retain logs long-term."


NEW QUESTION # 129
A company stores millions of PDF documents in an Amazon S3 bucket. The company needs to extract the text from the PDFs, generate summaries of the text, and index the summaries for fast searching.
Which combination of AWS services will meet these requirements? (Select TWO.)

Answer: C,D

Explanation:
Amazon Textract (E) automatically extracts text and structured data from scanned documents, such as PDFs.
Amazon Bedrock (B) offers access to LLMs (such as Amazon Titan or Anthropic Claude) for tasks like summarization and generating embeddings for search.
Workflow:
Amazon Textract extracts text from PDFs in S3.
Amazon Bedrock LLMs summarize the extracted text.
(Optional: Summaries can be indexed using Amazon OpenSearch or another search solution.) A (Translate) is for language translation, not extraction or summarization.
C (Transcribe) is for audio to text, not PDFs.
D (Polly) is for text-to-speech.
"Amazon Textract extracts text, forms, and tables from scanned documents... Bedrock provides generative AI models to perform summarization and other text generation tasks." (Reference: Amazon Textract, Amazon Bedrock, AWS GenAI RAG Reference)


NEW QUESTION # 130
A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.
The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.
The company needs a solution to ensure that the application does not generate biased recommendations.
Which solution will meet this requirement?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.
Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:
Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates Support data-centric mitigation, including improving dataset balance and representativeness After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:
The model learns from a more representative dataset
Disparate treatment recommendations based on demographics are reduced
Fairness is improved while maintaining clinical accuracy
Why the other options are not sufficient or aligned with AWS best practices:
B). Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.
C). Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.
D). Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.
AWS AI Study Guide References:
AWS Responsible AI principles: Fairness and Governance
Amazon SageMaker Clarify: bias detection and mitigation
AWS best practices for ML in high-risk domains such as healthcare


NEW QUESTION # 131
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?

Answer: C

Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
Reference:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg/what-is.html) AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)


NEW QUESTION # 132
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem

Answer:

Explanation:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)


NEW QUESTION # 133
......

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