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You are optimizing a prompt-tuned LLM for a financial institution's automated assistant. The assistant's main tasks include responding to customer inquiries about account balances, providing detailed transaction histories, and explaining complex financial products.
Which task should be prioritized for prompt-tuning to improve the model's performance in this domain?
A. Fine-tune the model for information retrieval tasks related to customer accounts.
B. Optimize for natural language generation to improve customer engagement.
C. Train the model to generate creative financial advice tailored to each customer.
D. Focus on improving the model's ability to generate financial market forecasts.
Answer: A
NEW QUESTION # 334
You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?
A. Increase the number of training epochs to further refine the model
B. Apply model quantization to reduce the complexity of the output
C. Use a higher learning rate to improve model convergence
D. Analyze token distribution patterns in the generated outputs
Answer: D
NEW QUESTION # 335
You are tasked with generating reproducible and consistent results for a particular GenAI model prompt during development and testing.
Which of the following is the primary model parameter to adjust in order to ensure that identical inputs produce identical outputs every time the model is run?
A. Max Tokens
B. Top-p (Nucleus Sampling)
C. Temperature
D. Random Seed
Answer: D
NEW QUESTION # 336
In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?
A. Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.
B. Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.
C. Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
D. Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.
Answer: B
NEW QUESTION # 337
When deploying AI assets in a deployment space, what is the most critical benefit of using deployment spaces in a large-scale enterprise environment?
A. Faster training times due to streamlined compute resources
B. Improved model accuracy through hyperparameter tuning
C. Better data labeling quality through automated labeling tools
D. Isolated environments to manage and monitor multiple model versions
Answer: D
NEW QUESTION # 338
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