IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A) Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
B) Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
C) Ensure that the synthetic data covers edge cases as well as common industry scenarios.
D) Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
E) Generate only a small amount of synthetic data to minimize computational costs and training time.

2. You are using IBM watsonx Prompt Lab to experiment with different versions of a prompt to generate accurate and creative responses for a customer support chatbot.
Which of the following best describes a key benefit of using Prompt Lab in the process of prompt engineering?

A) It provides a real-time environment for testing and refining prompts, helping to improve response quality.
B) It automatically generates prompts based on industry-specific data without any user input.
C) It allows users to generate AI models without the need for training data.
D) It limits the number of iterations a user can test to prevent overfitting the prompt to specific outputs.

3. In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?

A) Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.
B) Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.
C) Quantization always improves model performance but significantly increases model size.
D) Quantization maintains model accuracy but doubles the computation required for inference.

4. You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

A) The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.
B) The model occasionally generates offensive or inappropriate content when responding to user queries.
C) The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
D) The model is trained on open-source financial data, which results in slower response times during inference.
E) The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.

5. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.
Which of the following strategies would most effectively reduce bias in the model' responses?

A) Apply a post-processing filter that removes any gendered language after the model generates the response.
B) Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.
C) Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.
D) Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.

Solutions: