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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with generating high-quality responses from a large language model for a customer support application. You want to minimize the amount of provided examples while ensuring that the model generates relevant and specific answers.
Which of the following statements best differentiates between zero-shot and few-shot prompting in this context? (Select two)

A) Zero-shot prompting is better suited for tasks requiring domain-specific knowledge, while few-shot prompting is better for general knowledge tasks.
B) ct selection
C) Few-shot prompting involves fine-tuning the model on a specific dataset before generating output, whereas zero-shot prompting uses pre-trained knowledge without additional fine-tuning.
D) Zero-shot prompting does not require any examples in the input prompt, while few-shot prompting uses a limited number of examples to guide the model's response.
E) In zero-shot prompting, the model's response is generated purely based on pre-trained knowledge and the structure of the task, while in few-shot prompting, the examples provided offer the model additional context.
F) Few-shot prompting improves model performance for unfamiliar tasks by fine-tuning weights based on examples, while zero-shot prompting leaves the model weights unchanged.

2. You are developing a legal research assistant that uses Retrieval-Augmented Generation (RAG) to help lawyers find relevant case law. The system must provide case law that is not only relevant to the query but also semantically similar in meaning to the legal terminology used.
Given this scenario, which of the following retrievers would be the most appropriate choice for this application?

A) A sparse retriever that integrates exact matching of legal terms with synonym matching.
B) A rule-based retriever optimized for simple, pre-defined keyword mappings.
C) A dense retriever based on embeddings from a fine-tuned BERT model.
D) A sparse retriever based on keyword matching and BM25 algorithm.

3. 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 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.
B) Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers.
C) 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.
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.

4. You are tasked with designing a generative AI model to assist users in filling out a form that collects personal information, such as email addresses and phone numbers.
What is the most appropriate method to ensure the model can differentiate between required personal information and unnecessary sensitive data that should not be included in the output?

A) Use a post-generation filter to remove any text that appears to be personal information
B) Embed privacy-sensitive heuristics in the model's prompt to guide its behavior
C) Train the model exclusively on datasets that contain no personal information
D) Leverage regular expressions to filter out personal information in the prompt

5. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the model needs to generate a strictly factual output with minimal deviation from the prompt.
B) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
C) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
D) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.

Solutions: