IBM IBM Certified watsonx Generative AI Engineer - Associate C1000-185

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

1. In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

A) Greedy decoding works best when combined with temperature scaling to increase randomness
B) Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
C) Greedy decoding guarantees the highest overall probability for the output sequence
D) Greedy decoding is highly effective when multiple equally probable tokens are available at each step

2. You are working on a generative AI model that helps customers generate personalized responses to legal queries. The model is trained on a large corpus of publicly available legal documents. However, users often input personal information when interacting with the AI.
What is the most effective strategy to mitigate the risk of exposing personal information in the model's responses?

A) Use a privacy-preserving tokenization method to mask personal data in the input before feeding it into the model.
B) Limit the model's ability to retain memory of previous user inputs by resetting its state after every query.
C) Train the model on anonymized data to ensure that personal information is never present in the training set.
D) Apply a rule-based content filter to the model's outputs to remove any phrases that appear to contain personal information.

3. 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) "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]."
B) "Generate a summary for [New Contract]."
C) "Example 1: [First Contract]
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]."

4. You are using IBM's Tuning Studio to fine-tune a large-scale foundation model for a customer service chatbot. The goal is to optimize the model for performance in handling a wide variety of customer queries while minimizing computational costs. Before making any changes, you want to understand how Tuning Studio can help achieve your optimization goals.
Which of the following is the most significant benefit provided by Tuning Studio when optimizing a generative AI model?

A) Tuning Studio reduces the dataset size needed for training by implementing automated data augmentation strategies.
B) Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing.
C) Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively.
D) Tuning Studio allows the user to implement custom model architectures from scratch to meet specific task requirements.

5. When preparing a dataset for fine-tuning a large language model for a named entity recognition (NER) task, which of the following preprocessing steps is most critical for ensuring accurate entity classification?

A) Randomly shuffle the dataset before training to increase diversity
B) Remove rare entities to improve model performance on common entities
C) Use sentence segmentation to isolate each named entity in its own sentence
D) Ensure proper tokenization of the dataset according to the model's vocabulary

質問と回答：