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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q199-Q204):

NEW QUESTION # 199
You are tasked with optimizing a generative AI model in IBM watsonx.ai for an NLP-based application.
During the planning stage, which of the following data elements is most important to ensure the model generalizes well to real-world application usage?

A. High-dimensional feature vectors from a small, well-curated dataset
B. A large, diverse dataset with both structured and unstructured data
C. Only the structured data relevant to specific business rules
D. Pre-processed data with stop words removed to reduce noise

Answer: B

NEW QUESTION # 200
You are developing a solution that requires generating various reports using IBM Watsonx. To maintain efficiency and avoid redundant prompt creation, you decide to implement reusable prompts.
Which of the following best summarizes the value of using reusable prompts in this context?

A. They allow for faster deployment of new AI-driven solutions since the same prompt templates can be adapted for multiple tasks with minimal changes.
B. Reusable prompts eliminate the need for monitoring model performance since they consistently deliver accurate results.
C. Reusable prompts ensure that the AI model continues to evolve without additional training, as the prompts themselves become more refined over time.
D. Reusable prompts automatically adjust their structure depending on the input, eliminating the need for dynamic parameters or customization.

Answer: A

NEW QUESTION # 201
After prompt-tuning a language model, you notice that certain outputs are semantically correct but syntactically flawed.
Which of the following actions is most appropriate to resolve this issue and optimize the tuned model's performance?

A. Fine-tune the prompt template to emphasize grammar
B. Increase the model's training dataset size
C. Lower the learning rate during the tuning phase
D. Use a higher temperature during the generation process

Answer: A

NEW QUESTION # 202
You are developing a natural language generation (NLG) system for financial report summaries. The system needs to generate concise, high-quality summaries for different financial instruments. Tuning Studio is available as a tool to improve the performance of the model.
Which of the following capabilities of Tuning Studio is most helpful for improving the NLG system's performance in this domain-specific application?

A. It enables the fine-tuning of pre-trained models on custom datasets to improve accuracy and relevance to the specific domain.
B. It allows you to visually inspect and analyze the model's token-by-token generation process.
C. It provides real-time feedback on model performance during text generation.
D. It helps in selecting the most relevant datasets from a pre-existing library for fine-tuning the model.

Answer: A

NEW QUESTION # 203
You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.
What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?

A. Reduce the size of the training dataset
B. Increase the learning rate
C. Increase the number of epochs
D. Apply dropout during training

Answer: D

NEW QUESTION # 204
......

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