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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

A. Increasing the model's temperature parameter
B. Implementing zero-shot learning techniques
C. Using more open-ended prompts
D. Providing a list of credible sources in the prompt

Question 2

In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few-shot prompting techniques.
Which of the following best explains the key difference between zero-shot and few-shot prompting?

A. In zero-shot prompting, the model learns from a large number of examples during the inference stage, while in few-shot prompting, only a single example is used.
B. Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.
C. Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.
D. Zero-shot prompting provides the model with a few example tasks to help it understand the problem, while few-shot prompting provides no examples at all.

Question 3

While customizing an LLM in InstructLab to generate more human-like responses for a customer service chatbot, you notice that the responses are too formal and lack empathy.
Which of the following techniques will best address this problem and help tailor the model to generate more empathetic responses?

A. Change the decoder strategy from greedy decoding to beam search to increase response quality
B. Adjust the model's max sequence length to encourage longer responses
C. Apply transfer learning with a dataset containing casual language
D. Use prompt engineering to guide the model towards empathetic responses

Question 4

Which prompt engineering strategy is most effective for reducing the risk of generating biased content in a generative AI model?

A. Including specific disclaimers or guidelines in the prompt instructing the model to avoid biased language.
B. Adjusting the model's temperature to a lower value to generate more deterministic and consistent outputs.
C. Designing prompts that explicitly ask for balanced perspectives, such as "Provide both pros and cons of this issue."
D. Using a larger number of examples in the prompt to steer the model towards safer and more neutral outputs.

Question 5

While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

A. Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.
B. Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.
C. Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
D. Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.

Solutions: