AWS-C01

Evaluated on following tasks.

Exam Content

Define basic terms like AI, ML, deep learning, neural networks, cv, nlp...
Describe similarities and differences between AI, ML, GenAI, deep learning and agentic AI
Describe various types of inferencing: batch, real-time, async, serverless
Describe types of data in AI models: Labeled, unlabeled, tabular, time-series, (un)structured
Describe types of AI/ML learning: (un)supervised, reinforcement

Recognize where AI/ML can provide value: assist human decision making, scalability, automation
Determine when AI/ML solutions are not appropriate: cost-benefit analyses, specific outcome is needed instead of a prediction
Select the appropriate AI/ML techniques for specific use cases: regression, classification, clustering
Identify examples of real-world AI applications: computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting, knowledge bases, agentic AI.
Explain the capabilities of AWS managed AI/ML services: Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly
Identify when traditional ML models or foundation models are appropriate: regulatory concerns, explainability requirements, operational constraints

Describe and differentiate components of an AI/ML pipeline.
Describe sources of FM models: open source pre-trained models, training custom models
Describe methods to use a model in productionL managed API service, self-hosted API).
Identify relevant AWS services and features for each stage of an AI/ML pipeline: Amazon Bedrock, Amazon Quick, Kiro, SageMaker AI
Describe fundamental concepts of ML operations (MLOps): experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training
Describe model performance metrics: accuracy, precision, recall, F1 score and business metrics: cost per user, development costs, customer feedback, return on investment .