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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
AWS AI Services Overview26%- Describe Amazon Bedrock capabilities
  • 1. Describe model invocation, prompts, and responses
  • 2. Explain security, privacy, and compliance features
  • 3. Explain foundation models (FMs) available in Bedrock
  • 4. Identify Bedrock features (agents, knowledge bases, guardrails)
- 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
- Identify appropriate AWS AI services for given scenarios
  • 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
  • 2. Determine when to use pre-trained models vs. custom models
  • 3. Compare AWS AI services by capability and use case
- Describe AWS AI services for specific use cases
  • 1. Describe Amazon Polly, Rekognition, Transcribe, and Translate
  • 2. Explain AWS HealthScribe and other domain-specific services
  • 3. Identify services for NLP, computer vision, and recommendations
Responsible AI20%- 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
- 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. Describe model interpretability and explainability
  • 3. Recognize regulatory and ethical considerations
AI Application Development30%- Understand foundational concepts for building AI applications
  • 1. Explain function calling and tool use in AI applications
  • 2. Identify retrieval-augmented generation (RAG) concepts
  • 3. Describe prompt engineering techniques
  • 4. Explain prompts, tokens, and context windows
- Evaluate and optimize AI applications
  • 1. Assess application outputs for relevance and accuracy
  • 2. Implement caching and cost optimization techniques
  • 3. Apply evaluation frameworks for AI applications
  • 4. Identify performance bottlenecks and optimization strategies
- Implement AI applications using AWS services
  • 1. Use knowledge bases for context-aware responses
  • 2. Configure model parameters (temperature, top-p, top-k)
  • 3. Build applications using Amazon Bedrock APIs and SDKs
  • 4. Implement prompt templates and chain-of-thought reasoning
AI/ML Fundamentals24%- Explain the fundamental concepts of AI and ML
  • 1. Explain supervised and unsupervised learning
  • 2. Define generative AI and its key concepts
  • 3. Differentiate between AI, ML, and deep learning
  • 4. Identify common use cases for AI/ML
- 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
- 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)

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Amazon AWS Certified AI Practitioner Sample Questions (Q371-Q376):

NEW QUESTION # 371
A financial company has offices in different countries worldwide. The company requires that all API calls between generative AI applications and foundation models (FM) must not travel across the public internet.
Which AWS service should the company use?

Answer: B

Explanation:
AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, ensuring traffic does not traverse the public internet.
A is correct:
"AWS PrivateLink provides private connectivity to services across VPCs, keeping API traffic off the public internet." (Reference: AWS PrivateLink Overview) B (Amazon Q) is a generative AI assistant, not a network security/control tool.
C (CloudFront) is a CDN, not for private API calls.
D (CloudTrail) is for logging and monitoring, not secure connectivity.


NEW QUESTION # 372
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.

Answer:

Explanation:

Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)


NEW QUESTION # 373
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.

Answer: A

Explanation:
Comprehensive and Detailed
The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after AI assistant interaction).
Number of interactions (B) shows engagement, not sales impact.
Sentiment analysis (C) shows customer satisfaction but not revenue impact.
NLU accuracy (D) is a technical metric, not a business outcome.
Reference:
AWS Generative AI Use Cases - Measuring Business Value


NEW QUESTION # 374
A company wants to use its documents as a knowledge base for a large language model (LLM) in a Retrieval Augmented Generation (RAG) solution.
Which solution will meet these requirements?

Answer: A


NEW QUESTION # 375
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?

Answer: A

Explanation:
* ISO certifications apply to processes, frameworks, and systems - not individuals or every piece of software.
* When a company is ISO-certified, its development framework and governance processes comply with ISO standards for security, risk, or AI responsibility.
# Reference:
AWS Compliance Programs - ISO


NEW QUESTION # 376
......

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