IBM watsonx Generative AI Engineer - Associate 問題集 : C1000-185

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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 fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A) Add more general-purpose data to the fine-tuning dataset
B) Increase the number of layers in the model
C) Increase the model's batch size during training
D) Use domain-specific tokenization during fine-tuning

2. In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages.
Which of the following best describes when to use a vector database as part of the Retrieval-Augmented Generation (RAG) approach?

A) When the dataset consists mainly of structured tabular data and relational queries.
B) When there is a requirement to process large volumes of streaming data in real-time, and exact matching is the priority.
C) When there is a need to perform efficient keyword-based search on highly structured documents.
D) When the data consists of diverse unstructured documents, and you need to retrieve semantically similar content using dense vector representations.

3. You are tasked with designing a prompt template to assist a chatbot in generating professional email responses for customer service inquiries. The system should prioritize politeness, clarity, and conciseness.
What elements should be included in the prompt template to achieve the best results, considering optimal behavior of a large language model (LLM)? (Select two)

A) Instruct the model to limit responses to a specific character count
B) Ask the model to generate multiple versions of the response and rank them
C) Provide the customer's emotional context for better alignment with the tone
D) Include examples of informal customer service responses for variability
E) Specify the output tone as polite and professional

4. 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) Apply dropout during training
C) Increase the number of epochs
D) Increase the learning rate

5. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.
B) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.
C) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
D) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.

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