What does an AI practitioner do during the deployment phase of a generative AI model lifecycle?
Answer(s): C
During the deployment phase, the generative AI model is made available in a live production environment so end users or applications can access and use it.
A company maintains a large product catalog with detailed descriptions. The company wants to build an AIassistant to answer customer questions about the products. However, the company’s labeled training data is limited.Which solution will meet these requirements with the LEAST implementation effort?
Answer(s): A
A RAG architecture is the least-effort solution because it uses the existing product catalog as the knowledge source at runtime, so the foundation model can answer questions with current product information without requiring extensive labeled data or custom model training.
Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?
Retrieval Augmented Generation integrates large language models with external knowledge sources by retrieving relevant information at inference time and grounding the model’s responses in that data, improving accuracy and factual reliability.
Which AWS service or feature stores embeddings in a vector database for use with foundation models (FMs)and Retrieval Augmented Generation (RAG)?
Answer(s): B
Amazon OpenSearch Service provides a vector database capability to store and search embeddings, which are essential for powering Retrieval Augmented Generation (RAG) workflows and integrating with foundation models (FMs) in generative AI solutions.
A company designed an AI-powered agent to answer customer inquiries based on product manuals.Which strategy can improve customer confidence levels in the AI-powered agent's responses?
Providing direct links to the exact sections of the product manual that support each answer lets customers verify and trust the information, boosting confidence in the AI agent’s responses.
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