IBM C1000-185 Exam : IBM watsonx Generative AI Engineer - Associate

In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?

• A. Averaging the probabilities of multiple tokens at each step to generate a sequence
• B. Selecting the token with the highest probability at each step, without considering future possibilities
• C. Randomly selecting tokens from the top-k highest probability tokens at each step
• D. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens

You are managing a generative AI model deployment in IBM Watsonx and need to implement prompt versioning to ensure traceability and reproducibility of model behavior over time.
Which of the following strategies best enables versioning of prompts during deployment?

• A. Disabling versioning for prompts since it is not required for generative models.
• B. Using a source control system (e.g., Git) to track prompt changes alongside model code.
• C. Relying on model checkpointing to manage both model weights and prompts.
• D. Storing prompts in a flat file system and manually tracking versions.

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

In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?

• A. Embeddings are used to directly generate the textual responses in the output.
• B. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.
• C. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
• D. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.

You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?

• A. Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
• B. Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.
• C. Embeddings store the entire content of documents, which is then directly passed to the generative model.
• D. Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.