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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 using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

A) Use prompt engineering to provide more explicit instructions for the model
B) Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data
C) Adjust the token length limit in the model's configuration
D) Increase the number of fine-tuning epochs to ensure the model converges

2. Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?

A) Data Scientist
B) DevOps Engineer
C) Solution Architect
D) Data Engineer

3. In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?

A) Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
B) Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.
C) Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.
D) Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.

4. A team is using IBM InstructLab to customize a large language model (LLM) to automate responses in a healthcare chatbot application. The team wants to ensure the chatbot can handle user queries accurately, based on domain-specific instructions.
Which of the following correctly describes the role of the instruction optimization phase within the InstructLab workflow?

A) Instruction optimization involves retraining the model on a larger dataset for better accuracy.
B) Instruction optimization refines prompts to improve the model's ability to follow task-specific instructions.
C) Instruction optimization focuses on improving the dataset's quality by removing outliers and noise.

5. In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

A) Quantizing a model to 8-bit precision always results in a significant loss in performance, especially when working with language models or large generative AI architectures.
B) Post-training quantization is more resource-efficient than quantization-aware training, as it applies quantization after the model has been fully trained, eliminating the need for additional fine-tuning.
C) Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.
D) Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
E) Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.

質問と回答：