IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are using IBM watsonx's generative AI model to generate responses for a chatbot, and you want to ensure that the model stops generating text when it encounters a specific phrase like ":End of Response." Which of the following settings for stop sequences is most appropriate to achieve this goal?

A. Set the stop sequence to "End of Response"
B. Set the stop sequence to "\ n\ n"
C. Set the stop sequence to "<|stop|>"
D. Set the stop sequence to "STOP"

Question 2

You have successfully deployed a custom model using IBM Watson Machine Learning. Now, an application needs to access the model for inference.
Which of the following configurations will ensure that the application has appropriate access to the deployed model?

A. Implement OAuth2 authentication using IBM Cloud IAM for securing the API.
B. Assign the "viewer" role to the application's service account in Watson Machine Learning
C. Use the default IAM policies for all users in the IBM Cloud project
D. Create a public-facing API endpoint without authentication

Question 3

You have been using a pre-trained foundation model for a financial text summarization application. While the model is generating summaries that are generally accurate, it sometimes fails to handle domain-specific financial jargon. You are considering whether it's time to tune the model to optimize its performance for this task.
Which of the following conditions would most strongly justify tuning the foundation model for your specific use case?

A. The model's accuracy fluctuates based on the length of the input text.
B. The model generates output that is highly relevant to general topics but often misinterprets industry-specific terms like "leverage" or "derivative."
C. The model produces a high number of tokens in each output, which increases the cost of usage.
D. The model exhibits acceptable performance but occasionally generates off-topic responses unrelated to financial data.

Question 4

In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A. Top-k sampling selects the token with the highest probability, ignoring all other token options.
B. Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
C. Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
D. Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.

Question 5

You are working on a task that involves generating marketing copy using IBM Watsonx. The goal is to craft a prompt that leads to detailed and persuasive content about a new product launch.
Which of the following approaches would most likely result in high-quality, detailed, and contextually appropriate content?

A. Provide specific context and audience information: "Generate marketing copy for a new eco-friendly water bottle targeting health-conscious consumers. Include persuasive language and focus on the sustainability features of the product."
B. Use a very short prompt: "Generate marketing copy for a product launch."
C. Avoid specifying any constraints and rely on Watsonx's default model behavior: "Write product launch marketing content."
D. Use complex, technical jargon to generate highly specific content: "Produce syntactically dense prose with multifaceted aspects of ecological ramifications and commodification for a consumer base."

Solutions: