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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When optimizing the tuning process in IBM Watsonx Tuning Studio for a Generative AI model, which approach would best reduce training time and computational cost while maintaining model performance?

A) Perform full-scale retraining of the model for each new task to ensure maximum adaptability and accuracy.
B) Use all available training data, including unrelated examples, to ensure the model has a broad understanding of multiple tasks before tuning.
C) Increase the batch size and reduce the learning rate simultaneously to speed up the tuning process and minimize training iterations.
D) Focus the tuning on adjusting only the model's last few layers, which are responsible for task-specific outputs, while leaving the majority of the model unchanged.

2. You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

A) Use multiple stop sequences, including a period ."
B) Use a more distinct and unlikely stop sequence, such as "<END>"
C) Set the stop sequence to a punctuation mark like ";"
D) Increase the token limit to avoid premature cut-off

3. In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

A) Increasing the temperature parameter to make the model more deterministic and consistent in its responses.
B) Decreasing the number of tokens allowed in the output to encourage more creative responses.
C) Modifying the stop sequence to ensure the model responds with shorter answers for concise customer interactions.
D) Altering the tone of the prompt to make it more aligned with the brand's voice and customer expectations.

4. In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

A) Increase the model's randomness by adjusting the temperature parameter
B) Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
C) Reduce the model's token limit to restrict the amount of text generated
D) Introduce explicit instructions in the prompt to avoid generating personal information

5. You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A) "Generate a summary for [New Contract]."
B) "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
C) "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."
D) "Example 1: [First Contract]

Solutions: