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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Application Development30%- Implement AI applications using AWS services
  • 1. Configure model parameters (temperature, top-p, top-k)
  • 2. Use knowledge bases for context-aware responses
  • 3. Build applications using Amazon Bedrock APIs and SDKs
  • 4. Implement prompt templates and chain-of-thought reasoning
- Understand foundational concepts for building AI applications
  • 1. Explain prompts, tokens, and context windows
  • 2. Describe prompt engineering techniques
  • 3. Explain function calling and tool use in AI applications
  • 4. Identify retrieval-augmented generation (RAG) concepts
- 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
Topic 2: AWS AI Services Overview26%- Describe Amazon Bedrock capabilities
  • 1. Identify Bedrock features (agents, knowledge bases, guardrails)
  • 2. Explain security, privacy, and compliance features
  • 3. Explain foundation models (FMs) available in Bedrock
  • 4. Describe model invocation, prompts, and responses
- 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. 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 SageMaker capabilities
  • 1. Explain model training and deployment options
  • 2. Describe built-in algorithms and SageMaker JumpStart
  • 3. Recognize MLOps capabilities in SageMaker
  • 4. Identify SageMaker features for ML workflows
Topic 3: AI/ML Fundamentals24%- Understand the AI/ML lifecycle
  • 1. Recognize data collection, preprocessing, and feature engineering stages
  • 2. Identify phases of the ML lifecycle
  • 3. Describe model evaluation and deployment considerations
- 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)
- Explain the fundamental concepts of AI and ML
  • 1. Define generative AI and its key concepts
  • 2. Explain supervised and unsupervised learning
  • 3. Differentiate between AI, ML, and deep learning
  • 4. Identify common use cases for AI/ML
Topic 4: Responsible AI20%- Understand governance and compliance requirements
  • 1. Explain data privacy regulations affecting AI
  • 2. Describe model interpretability and explainability
  • 3. Recognize regulatory and ethical considerations
- 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
- 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 (Q253-Q258):

NEW QUESTION # 253
A company has a team of AI practitioners that builds and maintains AI applications in an AWS account. The company must keep records of the actions that each AI practitioner takes in the AWS account for audit purposes.
Which AWS service will meet these requirements?

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
AWS CloudTrail records API calls and user activity across AWS services, including:
* Who performed an action
* What action was taken
* When and from where the action occurred
AWS governance guidance identifies CloudTrail as the primary service for audit logging and accountability.
Why the other options are incorrect:
* AWS Config (B) tracks configuration changes, not user actions.
* Audit Manager (C) assesses compliance but does not record individual actions.
* Trusted Advisor (D) provides best-practice recommendations.
AWS AI document references:
* AWS CloudTrail Overview
* Auditing User Activity on AWS
* Governance and Compliance for AI Workloads


NEW QUESTION # 254
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning

Answer:

Explanation:


NEW QUESTION # 255
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?

Answer: B

Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
* Fine-Tuning Strategy:
* To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
* This allows the model to learn the desired behavior or response style based on the provided examples.
* Why Option A is Correct:
* Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
* Customization: Ensures that the model is fine-tuned to the specific requirements of the company' s data and desired outputs.
* Why Other Options are Incorrect:
* B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
* C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
* D. Train on journals and textbooks: Lacks the specific format and labeling required for fine- tuning.


NEW QUESTION # 256
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.

Answer: A

Explanation:
Comprehensive and Detailed
Explainable AI (XAI) techniques such as SHAP (SHapley values) or feature attribution provide transparency by showing which input factors influenced decisions.
A is not scalable for complex use cases.
C does not guarantee real interpretability.
D ignores the regulatory need for explainability.
Reference:
AWS SageMaker Clarify - Explainable AI


NEW QUESTION # 257
A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in.

Answer: D

Explanation:
The correct answer is A - Data residency. AWS defines data residency as ensuring that regulated or sensitive data-such as patient medical records under healthcare laws-remains physically stored and processed within specific national or regional boundaries. This aligns with regulatory frameworks such as HIPAA, GDPR, and country-specific health data protection acts. AWS allows customers to deploy all ML workloads, including Amazon SageMaker, Amazon Bedrock, and Amazon S3, within a chosen AWS Region, ensuring no cross- border data movement unless explicitly configured. Data quality (B) refers to accuracy and consistency, discoverability (C) relates to cataloging, and enrichment (D) refers to enhancing datasets. None of these address the compliance requirement to prevent data from leaving the country. Data residency is a core component of AWS's Shared Responsibility Model and foundational for healthcare AI compliance.
Referenced AWS Documentation:
* AWS Data Privacy Whitepaper - Data Residency Controls
* AWS Compliance Programs - Regional Data Handling Requirements


NEW QUESTION # 258
......

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