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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with configuring a generative model to assist legal professionals by generating contract clauses. The model should produce complete, contextually relevant clauses but should avoid overly long and redundant text. You must ensure that the generation process stops appropriately once a clause is completed.
Which of the following strategies is the most appropriate for configuring stopping criteria?

A) Use a stop sequence based on legal clause delimiters and impose a minimum token limit of 50.
B) Set a maximum token limit of 500 and implement a fixed length stopping criterion.
C) Set the top-k value to 1 and use the default maximum token limit, relying on the model's internal stopping criteria.
D) Use a beam search algorithm and impose a strict length restriction of 100 tokens.

2. What is the key difference between zero-shot and few-shot prompting when used in generative AI models like IBM Watsonx?

A) Few-shot prompting requires a model to have pre-trained examples of the task, while zero-shot does not.
B) Zero-shot prompting provides feedback to the model during inference, while few-shot does not allow model feedback.
C) In zero-shot prompting, the model is fine-tuned before answering, but in few-shot prompting, no fine-tuning occurs.
D) Zero-shot prompting does not provide any examples in the prompt, while few-shot prompting includes multiple task examples.

3. In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

A) A soft prompt is a fixed string of text used in fine-tuning, while a hard prompt adjusts dynamically based on input data.
B) Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.
C) Hard prompts are less efficient because they need to be re-trained with each task, while soft prompts are more versatile and adaptive across multiple tasks.
D) A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.

4. You are tasked with designing a LangChain-based AI workflow using watsonx.ai that incorporates multiple models for different tasks: document classification, entity extraction, and text generation. The final output should consist of a well-structured report that combines these processes.
What is the best strategy to orchestrate this workflow to ensure seamless integration of all tasks and a coherent final output?

A) Start with text generation using watsonx.ai and then refine the output through document classification and entity extraction.
B) Build a modular workflow where each task operates independently without passing data between them, ensuring each task can be validated separately before generating the final report.
C) Use document classification to filter the input data, followed by entity extraction to identify key terms, and then pass the refined information to watsonx.ai for text generation.
D) Run the document classification, entity extraction, and text generation tasks in parallel and merge the results at the end to produce the final report.

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

Solutions: