最新のC1000-185問題集で試験を準備する

IBM C1000-185 試験概要:

IBM C1000-185 試験シラバストピック:

IBM watsonx Generative AI Engineer - Associate 認定 C1000-185 試験問題：

1. You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

A) Setting the minimum tokens to 150 and maximum tokens to 50
B) Setting the maximum tokens to 50 and minimum tokens to 150
C) Setting no minimum token value but configuring the maximum tokens to 150
D) Setting the minimum tokens to 50 and maximum tokens to 150

2. You are tasked with optimizing a prompt-tuned large language model (LLM) using IBM Watsonx for a customer service chatbot. The chatbot needs to handle a variety of tasks, such as answering frequently asked questions (FAQs), providing detailed product descriptions, and troubleshooting user issues.
What is the most appropriate task to focus on during the initial tuning experiment?

A) Tune the model for text summarization, condensing user queries into shorter forms.
B) Fine-tune the model to generate product descriptions using longer contextual prompts.
C) Optimize the model for extractive question-answering from a predefined knowledge base.
D) Focus on prompt-tuning the model for multi-turn dialogue to simulate more natural conversations.

3. When designing a generative AI system to minimize the risk of producing hate speech or abusive content, which of the following strategies in prompt engineering is the most effective?

A) Implement real-time content moderation at the output level rather than relying on input prompts alone.
B) Use strict prompt filtering to block any input that contains offensive words.
C) Use offensive words in prompts to test the model's robustness in handling them.
D) Ensure that the model is trained on a larger dataset, even if the dataset contains diverse viewpoints, including some controversial opinions.

4. When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

A) Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.
B) Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.
C) Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.
D) Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.

5. When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A) It optimizes the model for multilingual capabilities by adding new embeddings.
B) It automatically increases the number of layers for more complex tasks.
C) It allows incremental training, saving computational resources by reusing checkpoints.
D) It expands the model's architecture to handle larger datasets.

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