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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When leveraging existing data for fine-tuning an LLM in IBM watsonx, you want to optimize the model for a highly specialized domain. You also want to generate additional synthetic data to augment your dataset.
Which of the following approaches would best help you achieve your goal?

A) Using unsupervised learning on your existing dataset without adding synthetic data
B) Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps
C) Relying exclusively on pre-trained general models without making domain-specific modifications
D) Manually crafting complex datasets by sampling individual instances from unrelated domains

2. A generative AI team is optimizing a model for text summarization. The team uses both hard prompts and soft prompts during training.
What is the primary reason a soft prompt might provide less explainability compared to a hard prompt?

A) Soft prompts involve complex embedding vectors, which are learned and difficult for humans to interpret directly.
B) Soft prompts are explicitly written by humans and include domain-specific instructions, making them harder to explain.
C) Soft prompts include manually designed templates that change dynamically, reducing transparency.
D) Soft prompts are used exclusively in unsupervised learning settings, where explainability is inherently lower due to lack of labeled data.

3. During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)

A) Use early stopping based on the validation set performance.
B) Increase the size of the model to better capture complex patterns in the training data.
C) Fine-tune the model for more epochs to ensure the model has fully learned from the training data.
D) Introduce dropout regularization during fine-tuning to prevent overfitting.
E) Remove all regularization and fine-tune the model on the training set until convergence.

4. You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision.
What does this likely indicate about the model's performance, and how should you approach further tuning?

A) The model's summaries are incomplete, indicating poor understanding of the source material; consider fine-tuning the pre-trained embeddings.
B) The model is generating too many irrelevant details; focus on improving precision.
C) The model is overly conservative, missing relevant details; focus on improving recall.
D) The model's length of generated summaries is too short, indicating underfitting.

5. In the context of prompt engineering, how does the repetition penalty parameter influence the behavior of a generative AI model, and which of the following scenarios demonstrates its correct usage?

A) A repetition penalty of 0.5 is ideal for generating creative text, as it promotes repetition of key phrases for emphasis
B) A repetition penalty of 1.0 encourages the model to introduce more varied word choices in its output
C) A repetition penalty higher than 1.0 will discourage the model from repeating the same words or phrases, while a value lower than 1.0 encourages repetition
D) Setting the repetition penalty to 1.0 disables any influence on repetition and allows the model to generate text without penalizing previously used tokens

Solutions: