← AWS Certified AI Practitioner
AWS · objective · 24% of the exam
Fundamentals of GenAI
Exam objectives are the official skill domains AWS Certified AI Practitioner tests. Below are the official AWS docs our questions on this objective cite — review them, then practise.
Sign in to track your progress on this objective.
Official references for this objective
-
Amazon Web Services — What are Generative AI Models? - Generative AI Models Explained - AWS
A model with a larger context window retains more of the previous conversation and provides more relevant responses.
-
Amazon Web Services — What is Generative AI? - Gen AI Explained - AWS
GANs work by training two neural networks in a competitive manner. The first network, known as the generator, generates fake data samples
-
Amazon Web Services — The role of vector databases in generative AI applications | AWS Database Blog
Used in this way, such vectors are referred to as embeddings
-
Amazon Web Services — Prompt engineering concepts - Amazon Bedrock
also known as few-shot prompting or in-context learning , where a shot corresponds to a paired example input and the desired output.
-
Amazon Web Services — What are Foundation Models? - Foundation Models in Generative AI Explained - AWS
the computational power required for foundation modeling has doubled every 3.4 months since 2012
-
Amazon Web Services — What is Embedding? - Embeddings in Machine Learning Explained - AWS
Embeddings reduce the number of dimensions by identifying commonalities and patterns between various features. This consequently reduces the computing resources and time required to process
-
Amazon Web Services — Comparing Retrieval Augmented Generation and fine-tuning - AWS Prescriptive Guidance
RAG allows you to build a question-answering system for your custom documents without fine-tuning. RAG can incorporate the latest documents in a few
-
Amazon Web Services — Prompt engineering for foundation models - Amazon SageMaker AI
If temperature=0 , the response is made up of only the highest probability words (greedy decoding)
-
Amazon Web Services — Tailoring foundation models for your business needs: A comprehensive guide to RAG, fine-tuning, and hybrid approaches | Artificial Intelligence
They can fine-tune the model every couple of months to keep it aligned with the company’s overall tone. Meanwhile, RAG can retrieve the latest product