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最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題 (Q308-Q313):

問題 #308
A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A. Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.
B. Apply model quantization to optimize the LLM for handling long legal documents more efficiently.
C. Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.
D. Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.

答案：A

問題 #309
You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

A. BLEU score
B. Token generation speed
C. Perplexity score
D. Log-likelihood of generated responses

答案：C

問題 #310
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. Focus on improving the model's ability to generate financial market forecasts.
B. Fine-tune the model for information retrieval tasks related to customer accounts.
C. Train the model to generate creative financial advice tailored to each customer.
D. Optimize for natural language generation to improve customer engagement.

答案：B

問題 #311
You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
B. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
C. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
D. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.

答案：C

問題 #312
You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)

A. Use a top-p (nucleus) sampling value of 0.95 for diverse, relevant outputs
B. Set the temperature to 0.1 for highly deterministic responses
C. Apply a low top-k value (e.g., k=10) to restrict randomness
D. Set a high penalty for repetition to encourage varied recommendations
E. Increase the temperature to 1.5 to maximize creativity in suggestions

答案：A,C

問題 #313
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C1000-185認證考試解析，C1000-185最新考題

C1000-185最新考題，C1000-185權威考題

最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題 (Q308-Q313):

完美的C1000-185認證考試解析 |高通過率的考試材料|高通過率的C1000-185：IBM watsonx Generative AI Engineer - Associate

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