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What is a key benefit of using Prompt Lab in IBM Watsonx to build reusable prompts for generative AI applications?
A. Prompt Lab uses machine learning to automatically improve prompts over time without the need for developer intervention.
B. Prompt Lab automatically adjusts prompts based on the user's previous interactions, ensuring personalized output.
C. Prompt Lab allows for the creation of templates with dynamic placeholders, making it easier to reuse prompts in various contexts without modification.
D. Prompt Lab uses predefined responses to train the model, eliminating the need for further prompt experimentation.
Answer: C
NEW QUESTION # 325
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?
A. It reduces the overall size of the dataset by filtering out irrelevant data.
B. It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
C. It ensures that the model only focuses on specific medical specialties by eliminating other categories.
D. It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
Answer: D
NEW QUESTION # 326
You are part of a team building an AI-powered assistant that helps software developers by answering technical queries. To handle the vast amount of technical documentation efficiently, the team has chosen to implement the Retrieval-Augmented Generation (RAG) pattern. LangChain is used to construct the retrieval-generation pipeline, and SingleStore is employed to store the document embeddings. You need to ensure that the integration of LangChain with SingleStore allows for real-time retrieval of relevant technical documentation.
Which of the following configurations should be used to ensure an optimal RAG implementation with LangChain and SingleStore?
A. Use LangChain to embed documents into a vector space and store these embeddings in SingleStore. During inference, embed the query in the same vector space and retrieve documents using vector similarity search.
B. Use LangChain to retrieve pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.
C. Use LangChain to embed both queries and documents into the same vector space and store these embeddings in SingleStore. During inference, retrieve documents using SQL queries based on keyword similarity.
D. Use LangChain to retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
Answer: A
NEW QUESTION # 327
You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?
A. Increase the token limit to avoid premature cut-off
B. Use multiple stop sequences, including a period ."
C. Set the stop sequence to a punctuation mark like ";"
D. Use a more distinct and unlikely stop sequence, such as "<END>"
Answer: D
NEW QUESTION # 328
In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?
A. Randomly selecting tokens from the top-k highest probability tokens at each step
B. Averaging the probabilities of multiple tokens at each step to generate a sequence
C. Selecting the token with the highest probability at each step, without considering future possibilities
D. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
Answer: C
NEW QUESTION # 329
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