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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with deploying two generative AI systems: one for document summarization and another for question-and-answer (Q&A). Both systems need to handle large volumes of unstructured text, but they differ in their response time requirements and interaction with users.
Which deployment strategy would be most appropriate for this scenario?

A. Use a rule-based model for summarization and a machine learning-based model for Q&A, combining them into a single service endpoint for deployment.
B. Use a single deployment strategy where both the summarization and Q&A systems are integrated into a shared model that processes all requests through the same inference pipeline.
C. Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers.
D. Deploy the summarization model as a batch processing system, while the Q&A system is deployed as an on-demand service using a low-latency inference model.

Question 2

After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

A. ROUGE score; adjust the token generation limit to ensure longer outputs.
B. BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
C. F1 score; increase the training dataset size to improve overall accuracy.
D. Perplexity score; apply additional language model fine-tuning on grammatical correctness.

Question 3

A healthcare organization is deploying a generative AI model to assist doctors in generating patient reports based on medical notes and test results. The organization has strict compliance requirements regarding patient data privacy (e.g., HIPAA in the U.S.) and needs to ensure that the model outputs are governed under their AI policies. You are tasked with integrating AI governance policies into the deployment to meet both ethical and legal standards.
Which AI governance measure is most important to implement during the deployment phase of this AI solution, given the healthcare organization's need for compliance with patient privacy regulations?

A. Deploying an explainability module to allow doctors to see the reasons behind each AI-generated suggestion, ensuring ethical use of the model.
B. Implementing a system to track and log all model-generated inferences, flagging any that suggest potential bias or non-compliance with privacy regulations.
C. Ensuring that the model's training data includes only anonymized patient records to avoid potential data breaches.
D. Requiring doctors to manually review every AI-generated report before it is finalized to meet compliance with medical standards.

Question 4

Which of the following represents the most effective use of example input prompts within IBM Watsonx's Prompt Lab for generating a high-quality response?

A. Crafting prompts with very specific and detailed instructions, ensuring the model follows a strict framework for the desired response.
B. Using prompts with complex sentence structures and advanced terminology to challenge the model's understanding capabilities.
C. Writing prompts that are extremely vague, allowing the model to freely interpret the input and generate diverse responses.
D. Using example prompts that introduce multiple topics at once to evaluate how well the model handles multitasking during response generation.

Question 5

You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

A. Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.
B. Using temperature scaling to increase randomness and reduce token usage.
C. Revising the prompt to make it more specific by narrowing the scope of expected responses.
D. Reducing the token limit in the model configuration to restrict the length of responses.

Solutions: