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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Responsible AI20%- Understand governance and compliance requirements
  • 1. Recognize regulatory and ethical considerations
  • 2. Describe model interpretability and explainability
  • 3. Explain data privacy regulations affecting AI
- Explain the principles of responsible AI
  • 1. Identify potential biases in AI/ML models
  • 2. Define fairness, transparency, and privacy in AI systems
  • 3. Describe strategies for bias mitigation
- Implement responsible AI best practices
  • 1. Implement appropriate guardrails for AI applications
  • 2. Apply human oversight in AI decision-making
  • 3. Evaluate AI outputs for quality and safety
Topic 2: AWS AI Services Overview26%- Describe Amazon Bedrock capabilities
  • 1. Describe model invocation, prompts, and responses
  • 2. Explain foundation models (FMs) available in Bedrock
  • 3. Explain security, privacy, and compliance features
  • 4. Identify Bedrock features (agents, knowledge bases, guardrails)
- Describe Amazon SageMaker capabilities
  • 1. Recognize MLOps capabilities in SageMaker
  • 2. Identify SageMaker features for ML workflows
  • 3. Describe built-in algorithms and SageMaker JumpStart
  • 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. Compare AWS AI services by capability and use case
  • 3. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
- Describe AWS AI services for specific use cases
  • 1. Explain AWS HealthScribe and other domain-specific services
  • 2. Identify services for NLP, computer vision, and recommendations
  • 3. Describe Amazon Polly, Rekognition, Transcribe, and Translate
Topic 3: AI Application Development30%- 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. Use knowledge bases for context-aware responses
  • 4. Configure model parameters (temperature, top-p, top-k)
- 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
- 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
Topic 4: AI/ML Fundamentals24%- Identify foundational terminology and definitions
  • 1. Define common ML metrics (accuracy, precision, recall, F1 score)
  • 2. Define models, algorithms, and parameters
  • 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. Identify phases of the ML lifecycle
  • 3. Describe model evaluation and deployment considerations
- Explain the fundamental concepts of AI and ML
  • 1. Identify common use cases for AI/ML
  • 2. Differentiate between AI, ML, and deep learning
  • 3. Define generative AI and its key concepts
  • 4. Explain supervised and unsupervised learning

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

NEW QUESTION # 122
A company has a large amount of unlabeled data. The company wants to group the data based on feature similarities.
Which algorithm will meet this requirement?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS machine learning fundamentals classify K-means as an unsupervised learning algorithm used to group unlabeled data into clusters based on feature similarity and distance metrics. Because the data is unlabeled and the goal is grouping rather than prediction, K-means is the most appropriate choice.
Why the other options are incorrect:
* XGBoost is a supervised learning algorithm that requires labeled data.
* DeepAR forecasting is designed for time series forecasting.
* Linear learner is typically used for supervised regression or classification tasks.
AWS AI Study Guide References:
* AWS unsupervised learning concepts
* AWS clustering algorithms overview


NEW QUESTION # 123
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML

Answer:

Explanation:

Explanation:
Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.
(Reference: AWS Certified AI Practitioner Official Study Guide)
Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.
(Reference: AWS Machine Learning Overview)
Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.
(Reference: AWS Deep Learning on AWS)


NEW QUESTION # 124
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

Explanation:
Retrieval Augmented Generation (RAG) relies on vector-based semantic search to retrieve relevant information from a knowledge base and provide it as context to a large language model. AWS documentation explains that RAG solutions require documents to be converted into numerical vector representations called embeddings, which capture semantic meaning rather than exact keyword matches.
In this scenario, creating embeddings from document chunks is essential. AWS recommends splitting documents into smaller chunks before generating embeddings to improve retrieval accuracy and relevance.
Chunking ensures that only the most pertinent sections of a document are retrieved and passed to the LLM, reducing noise and improving response quality.
AWS generative AI services, including Amazon Bedrock-based RAG architectures, use embeddings to store document representations in vector databases. During inference, user queries are also converted into embeddings, and similarity search is performed to identify the most relevant document chunks. This process enables the LLM to ground its responses in company-specific knowledge, reducing hallucinations and improving factual accuracy.
The other options do not fulfill RAG requirements. Encrypting documents addresses security but does not enable semantic retrieval. Labeling metadata can support filtering but does not replace embeddings. One- hot encoding is inefficient for large text corpora and does not capture semantic relationships.
AWS documentation consistently identifies embedding generation as a foundational step in RAG architectures, making this the correct solution.


NEW QUESTION # 125
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: B

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 # 126
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions. The company wants to evaluate the AI solution's return on investment over time.

Answer: B

Explanation:
The correct answer is C, as conversion rate (how many users upgrade to premium) and retention rate (how many stay subscribed) are primary indicators of a generative AI system's business impact and ROI.
According to AWS documentation, evaluating AI performance should go beyond technical accuracy to include business metrics that show real-world value. For a premium service, higher conversion implies successful personalization that attracts free users, while strong retention reflects sustained engagement and user satisfaction. Metrics like ARPU or query reduction are secondary indicators. AWS emphasizes outcome- based measurement to ensure AI initiatives contribute measurable ROI tied to customer adoption, loyalty, and profitability. Therefore, tracking conversion and retention gives the clearest long-term measure of financial success from generative AI.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - ML Business Metrics
AWS AI Adoption Framework - Value Realization and ROI Tracking


NEW QUESTION # 127
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