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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q244-Q249):

NEW QUESTION # 244
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. Generating synthetic data that overrepresents the simplest task cases to reduce computational load
B. Using domain-specific templates to generate synthetic data that reflects the target use case
C. Randomly generating sentences without adhering to the task-specific instructions
D. Avoiding any post-processing or filtering of synthetic data to retain diversity

Answer: B

NEW QUESTION # 245
You are tasked with building a Retrieval-Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure.
How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)

A. Train a domain-specific embedding model using legal documents to better capture the nuances of legal terminology.
B. Apply dimensionality reduction techniques like PCA to compress embeddings and improve retrieval speed.
C. Rely solely on word-level embeddings to capture the meaning of legal phrases and concepts.
D. Use an unsupervised learning approach to generate embeddings, as labeled data is not necessary for improving retrieval performance.
E. Integrate additional metadata (e.g., document date, author) into the embedding representation to improve retrieval.

Answer: A,E

NEW QUESTION # 246
How can developers best ensure that prompt templates are version-controlled and any changes to the templates can be tracked efficiently in a collaborative team environment?

A. Implement a versioning system for prompt templates in a source control tool like Git
B. Update prompt templates directly in production and rely on error logs for tracking changes
C. Use a centralized cloud storage system that automatically saves all versions of the prompt templates
D. Manually keep a changelog of prompt template modifications in a shared document

Answer: A

NEW QUESTION # 247
IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.
Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?

A. It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.
B. It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.
C. It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
D. It significantly reduces the computational costs associated with model fine-tuning by only updating the model's parameters relevant to the specific task, preserving the general knowledge of the base model.

Answer: D

NEW QUESTION # 248
You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

A. Reducing the token limit in the model configuration to restrict the length of responses.
B. Using temperature scaling to increase randomness and reduce token usage.
C. Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.
D. Revising the prompt to make it more specific by narrowing the scope of expected responses.

Answer: D

NEW QUESTION # 249
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