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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q59-Q64):
NEW QUESTION # 59
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?
- A. Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
- B. Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
- C. Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
- D. Fine-tuning the generative model on the entire document corpus without retrieval components.
Answer: A
NEW QUESTION # 60
In IBM Watsonx Generative AI, top-p sampling (nucleus sampling) is a parameter that influences the decoding process.
Which of the following best describes how the top-p parameter works during response generation?
- A. Top-p sampling adjusts the probability threshold dynamically, allowing the model to sample tokens until the cumulative probability reaches p.
- B. Top-p sampling prioritizes tokens based on their frequency in the training dataset, ignoring their conditional probabilities.
- C. Top-p sampling ensures that only the top 1% most probable tokens are chosen, regardless of context.
- D. Top-p sampling selects the next token only from a fixed number of top tokens, ensuring the most predictable outcome.
Answer: A
NEW QUESTION # 61
You are working on deploying a generative AI model into production. The goal is to ensure that different versions of prompts can be tracked and rolled back in case of degradation in the model's performance.
Which of the following strategies would best address versioning for deployment?
- A. Integrate prompt versioning into the model deployment pipeline using automated versioning tools like Git.
- B. Use manual version control through logging and local storage.
- C. Use endpoint monitoring tools only, without any versioning approach, to track and assess prompt changes in production.
- D. Maintain different versions of the model and prompts by duplicating them across multiple endpoints without a centralized repository.
Answer: A
NEW QUESTION # 62
While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?
- A. Switching from a narrative-style prompt to a bulleted list format.
- B. Including multiple tasks in a single prompt to maximize efficiency.
- C. Using stop tokens early in the prompt to minimize generation length.
- D. Breaking complex prompts into simpler, sequential prompts.
Answer: C
NEW QUESTION # 63
In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?
- A. Randomly generating sentences without adhering to the task-specific instructions
- B. Using domain-specific templates to generate synthetic data that reflects the target use case
- C. Generating synthetic data that overrepresents the simplest task cases to reduce computational load
- D. Avoiding any post-processing or filtering of synthetic data to retain diversity
Answer: B
NEW QUESTION # 64
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