C1000-185 : IBM watsonx Generative AI Engineer - Associate

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

1. While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

A) Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.
B) Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
C) Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.
D) Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.

2. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A) Top-k sampling selects the token with the highest probability, ignoring all other token options.
B) Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
C) Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
D) Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.

3. You are tasked with fine-tuning a large language model (LLM) using IBM's InstructLab to improve performance for a specific customer service task. The goal is to enhance the model's ability to answer questions related to account management and customer complaints.
Which of the following actions is NOT a component of the fine-tuning process in InstructLab?

A) Defining specific task instructions that the model will follow during inference
B) Selecting and preprocessing a representative dataset of customer interactions for training
C) Tuning the learning rate to prevent overfitting during the fine-tuning process
D) Directly adjusting the model's architecture to increase the number of attention heads in the transformer

4. When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)

A) Increase the model's complexity by adding more layers
B) Increase the number of training epochs
C) Decrease the batch size
D) Reduce the learning rate
E) Enable early stopping

5. You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A) "Generate a summary for [New Contract]."
B) "Example 1: [First Contract]
C) "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
D) "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."

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