AWS Certified AI Practitioner AIF-C01 Prep

A real case study analysis of how SageMaker works, from notebooks to endpoints, with a focus on exam-relevant features.

We'll show you how to access models from providers like Anthropic and AI21, and when to choose Bedrock for your use case.

A practical lab walkthrough for image and video analysis, including object detection and facial analysis.

Learn the key differences between these services and how to implement them for transcription and voice applications.

A problem-solving session on using Comprehend for sentiment analysis, entity recognition, and topic modeling.

Understand how Kendra uses ML to provide natural language search across your organization's documents.

A real-world example of how Forecast improves demand planning and inventory management.

Step-by-step guide to creating a chatbot with Lex and integrating it with other AWS services.

A practical breakdown of pricing models for key AI services and tips to keep your exam practice and projects within budget.

We'll map common business problems to the right ML approach and AWS service, a key skill for the exam.

Learn how SageMaker Automatic Model Tuning works and why it's a game-changer for performance.

A clear guide to accuracy, precision, recall, and F1 scores without getting lost in the weeds.

A hands-on walkthrough of deploying a trained model and testing it with real-time inference.

When and why to use batch transform jobs instead of real-time endpoints, with a practical use case.

A real case study analysis of building a repeatable, automated workflow for model training and deployment.

Learn what model drift is and how Amazon SageMaker Model Monitor helps you catch it early.

A deep dive into security best practices, including how to encrypt data for training and control access to models.

We'll discuss data residency, GDPR, and how AWS helps you meet compliance requirements for AI.

A critical look at identifying bias in datasets and models, and using tools like SageMaker Clarify.

A problem-solving session combining computer vision, NLP, and speech services for a complex business solution.

Learn how to deploy models to edge devices for low-latency, offline inference.

Step-by-step guide to building event-driven AI pipelines that automate complex workflows.

A comparative analysis to help you decide when to use a pre-built model from the marketplace versus a custom solution.

We'll review tricky question patterns, ambiguous wording, and the 'best practice' trap that trips up many candidates.

A brief look at emerging trends and services to keep an eye on after you've passed your exam.

A timed set of 65 unique questions that mimics the real exam environment to test your knowledge and timing.

We'll go through every question from the practice exam, explaining the correct answer and why the distractors are wrong.

A downloadable one-page guide to the most important service limits, features, and use cases to remember for the exam.

A strategy session on how to spend your final 7 days for maximum retention and confidence.

A practical guide to scheduling your test, setting up your testing environment for a proctored exam, and managing nerves.

A real-world project idea to apply your new skills, with a step-by-step outline to get you started.

We'll map out potential career trajectories and the next certifications to consider, like Machine Learning Specialty.

A curated list of AWS communities, forums, and resources to continue your learning journey and connect with peers.

A quick summary of everything we've covered and a clear action plan for your next steps after finishing the course.

An overview of limited-time sandbox environments and workshops you can use for extra hands-on practice without full deployment.