IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working with IBM Watsonx to develop a generative AI solution that automatically generates product descriptions for an e-commerce website. The descriptions need to be concise, factual, and include important product features like size, color, and material.
Which prompt design approach would best ensure the output meets these requirements?

A) "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material."
B) "Write a summary that provides information on each product, making the content engaging, humorous, and memorable."
C) "Generate a product description that highlights the unique aspects of the product and uses emotional language to engage the reader."
D) "Generate a creative and imaginative product description for the items listed below."

2. Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?

A) Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
B) Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.
C) Enhancing the model's ability to generalize across unseen data by training it specifically on the variations included in the prompt template.
D) Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.

3. You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A) Reduce memory utilization and maintain BLEU and ROUGE-L scores
B) Maximize BLEU score and reduce memory utilization
C) Focus on improving the ROUGE-L score while increasing memory utilization
D) Increase memory utilization to reduce BLEU and ROUGE-L scores

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

5. When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?

A) Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.
B) Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.
C) Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
D) Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.

Solutions: