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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with building a customer service chatbot powered by a generative AI model for a large financial institution. Customers often provide sensitive personal and financial information in their queries.
What is the best method to ensure the model does not generate responses containing sensitive personal data?

A. Implement differential privacy to reduce the likelihood that sensitive data from user inputs is exposed in the model's responses.
B. Use supervised fine-tuning to train the model on data that excludes all personally identifiable information (PII).
C. Limit the model's ability to generate personalized responses by focusing on generalized outputs, reducing the risk of data exposure.
D. Restrict the model to only respond to predefined template-based answers to eliminate the risk of personal data being generated.

Question 2

You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

A. Setting no minimum token value but configuring the maximum tokens to 150
B. Setting the minimum tokens to 150 and maximum tokens to 50
C. Setting the minimum tokens to 50 and maximum tokens to 150
D. Setting the maximum tokens to 50 and minimum tokens to 150

Question 3

You are working on optimizing a large language model (LLM) using quantization techniques. Your goal is to reduce memory usage while maintaining as much of the model's original accuracy as possible.
What is a common challenge faced when applying quantization to LLMs, and how can it be mitigated?

A. Embedding layers in LLMs are difficult to quantize, so it's best to skip quantization for these layers entirely.
B. Quantization causes a drastic reduction in training time, but increases memory usage. Use sparse quantization to address this.
C. Quantization may cause a significant drop in model accuracy, especially in embedding layers. Using quantization-aware training can help mitigate this.
D. LLMs are inherently resistant to quantization, so switching to a smaller model architecture is the only viable solution.

Question 4

You are using a generative AI model in a healthcare application to generate personalized treatment recommendations based on patient data.
Which of the following scenarios represent valid concerns related to model risks when deploying the AI in this setting? (Select two)

A. The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
B. The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.
C. The model generates text that includes private patient information, violating data privacy regulations.
D. The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
E. The model fails to generate treatment recommendations for some patients due to exceeding token limits during inference.

Question 5

You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

A. Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
B. Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
C. Store all prompt versions directly in the model's code repository, updating the main branch with each new version.
D. Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.

Solutions: