Study the official exam domains. Each lesson teaches the concept from the vendor exam guide and first-party docs.
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Enroll in This CourseArtificial intelligence is the umbrella. Machine learning is one way to get there. Deep learning is the machine learning method that uses neural networks with many hidden layers. A first-time reader only needs one rul...
The exam asks you to pick a learning type from the data you have and the output you want. Supervised learning needs labeled examples. Unsupervised learning finds structure in unlabeled examples. Reinforcement learning...
A model is only as useful as the file you give it. The exam asks you to name the data before you name the technique. Labeled versus unlabeled decides the learning type. Structured versus unstructured decides how much...
Training builds the model. Inference is the call that uses it. AWS also writes inferencing for the same idea. Batch inference scores a pile of records when you can wait. Real-time inference answers one request with lo...
Domain 1 asks you to recognize the job before you recognize the service. Computer vision reads images and video. Natural language processing reads and writes language. Speech turns audio into text or text into audio....
The exam does not treat every problem as a model. Task 1.2 asks you to recognize when AI or machine learning adds value, and when it does not. Value looks like help for a human decision, a process that must scale past...
Task 1.2 asks you to explain the managed AI and ML services AWS already trained for common jobs. Amazon SageMaker AI is the workshop where you build, train, and host your own models. Amazon Transcribe, Amazon Translat...
Task 1.3 asks you to walk the machine learning pipeline in order. Data collection, exploratory data analysis, preprocessing, feature engineering, training, hyperparameter tuning, evaluation, deployment, and monitoring...
The last Domain 1 task is operational. Where does the model come from. How does an application call it. How do you know the model is good. How do you know the project is worth it. Open-source and pre-trained checkpoin...
Task 2.1 starts with the raw units a generative model actually consumes. A prompt is not one blob of English. The model splits that text into tokens, turns meaning into embeddings, and compares those embeddings as vec...
Task 2.1 asks you to name the model class that made generative AI practical. A foundation model is a large deep learning network trained on a broad, mostly unlabeled corpus so it can do many general jobs from a prompt...
Task 2.1 lists prompt engineering next to tokens and foundation models because a generative model is open-ended. One word can produce a long reply. A useful reply needs instruction, context, and a stated shape. Prompt...
Task 2.1 asks for the foundation model lifecycle. Data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback. The expensive step is pre-training. Fine-tuning changes weights on a...
Task 2.1 pairs multi-modal models with diffusion models because not every generative job is next-token text. A multimodal model accepts or produces more than one kind of signal. Text, image, video, speech, or a mix. D...
Task 2.2 asks why a firm would use generative AI and how to pick a model. The exam's advantage words are adaptability, responsiveness, and simplicity. The selection words are model type, performance, capability, const...
Task 2.2 names four disadvantages. Hallucinations, interpretability, inaccuracy, and nondeterminism. They all come from the same engine you learned in lesson 2.2. The model samples the next token from a probability di...
Task 2.3 asks you to name the AWS service that turns foundation models into an application API. Amazon Bedrock is that service. Fully managed. You pick a model, send a prompt, and receive a completion. You do not stan...
Task 2.3 lists Amazon Q next to PartyRock, an Amazon Bedrock Playground. They sit at opposite ends of the same idea. PartyRock is how a person learns prompting and model choice in a browser with no AWS account. Amazon...
Task 2.3 names Amazon SageMaker JumpStart beside Bedrock, PartyRock, and Amazon Q. JumpStart is the machine-learning hub inside SageMaker AI. You discover, deploy, fine-tune, and evaluate foundation models, including...
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