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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A) Word2Vec
B) Bag-of-Words (BoW)
C) Latent Dirichlet Allocation (LDA)
D) One-Hot Encoding

2. You are working on optimizing a generative AI model that will handle large-scale text generation tasks. The current model is slow during inference, and you need to improve its performance without increasing operational costs. You decide to use IBM Tuning Studio for optimization.
Which of the following is the most significant benefit of using Tuning Studio in this scenario?

A) It optimizes hyperparameters such as learning rate and batch size to reduce computational overhead during inference.
B) It automatically scales the model up or down depending on the input data size.
C) It pre-loads commonly used datasets, reducing the need for data handling during the training process.
D) It provides guidance on reducing the number of parameters in the model to improve inference speed.

3. You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

A) Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
B) Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
C) Store all prompt versions directly in the model's code repository, updating the main branch with each new version.
D) Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.

4. You are developing a chatbot application that uses IBM watsonx to assist users with customer support. The chatbot needs to respond with accurate, up-to-date information from a large corpus of documents. The documents include unstructured data, such as support tickets, product guides, and troubleshooting steps. Due to the need for highly specific answers, the system should be able to retrieve relevant information from the document base, while also generating human-like responses using a large language model (LLM).
In this scenario, which configuration should be used to ensure the chatbot retrieves the most relevant context before generating a response, and why would this approach be beneficial?

A) Use a knowledge graph-based retriever integrated with the LLM, leveraging structured relationships between data points.
B) Use a term-based retriever with an inverted index and perform keyword-based search, followed by minimal model tuning for response generation.
C) Use a sparse retriever and a standard SQL database, then fine-tune the language model to perform context matching.
D) Use a dense retriever with a vector database for embedding-based retrieval, followed by using the LLM for context generation.

5. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the model needs to generate a strictly factual output with minimal deviation from the prompt.
B) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
C) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
D) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.

Solutions: