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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model.
Which of the following statements best describes the key difference between zero-shot and few-shot prompting?

A) Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.
B) Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.
C) Zero-shot prompting provides the model with examples, while few-shot prompting does not.
D) Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.

2. Which of the following factors is most likely to contribute to biased outputs in a generative AI system?

A) Insufficient diversity in the training dataset used to train the model.
B) Prompt length that exceeds the model's context window.
C) The use of too many input parameters in the prompt.
D) Repeatedly using the same prompt for multiple generations.

3. When planning data elements for optimizing a generative AI model's performance in IBM watsonx, which of the following strategies should be prioritized to ensure data quality and model accuracy?

A) Ensure all data used is unstructured to allow for maximum model flexibility.
B) Leverage data augmentation techniques only on the smallest available dataset.
C) Focus on using only labeled data, as generative AI models do not perform well on unlabeled data.
D) Prioritize balanced datasets to minimize model bias and enhance generalization.

4. You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

A) Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.
B) Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
C) Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.
D) Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.

5. When debating the drawbacks of soft prompts in a generative AI application, which of the following is the most significant challenge compared to hard prompts?

A) Soft prompts introduce more complexity during the training phase, as the model must learn embeddings that are not inherently interpretable by humans.
B) Soft prompts offer simpler debugging processes because the learned embeddings are directly linked to specific model behaviors and outputs.
C) Soft prompts require more human intervention during generation because they depend on predefined patterns and rules for guidance.
D) Soft prompts significantly limit the flexibility of the model because they are tied to specific tasks, unlike hard prompts which can generalize to various scenarios.

Solutions: