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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working on a generative AI model that helps customers generate personalized responses to legal queries. The model is trained on a large corpus of publicly available legal documents. However, users often input personal information when interacting with the AI.
What is the most effective strategy to mitigate the risk of exposing personal information in the model's responses?

A) Use a privacy-preserving tokenization method to mask personal data in the input before feeding it into the model.
B) Limit the model's ability to retain memory of previous user inputs by resetting its state after every query.
C) Train the model on anonymized data to ensure that personal information is never present in the training set.
D) Apply a rule-based content filter to the model's outputs to remove any phrases that appear to contain personal information.

2. 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) Revising the prompt to make it more specific by narrowing the scope of expected responses.
C) Using temperature scaling to increase randomness and reduce token usage.
D) Reducing the token limit in the model configuration to restrict the length of responses.

3. In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?

A) When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.
B) When the task requires highly domain-specific knowledge that the model has not been exposed to before.
C) When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.
D) When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.

4. A team is implementing a Retrieval-Augmented Generation (RAG) system for document search and retrieval. Their goal is to enable users to retrieve contextually relevant documents from a large, unstructured text corpus. They are considering using a vector database to handle this task.
In which scenario is a vector database the most appropriate choice for storing and retrieving documents?

A) When documents need to be retrieved based on semantic similarity to the user's query, even if the exact terms are not matched.
B) When exact match search is required, such as finding documents containing specific keywords or phrases.
C) When the system must support real-time updates and frequent data modifications, ensuring that query results always reflect the latest state of the data.
D) When all documents are structured and can be queried using traditional SQL queries, focusing on specific fields and categories.

5. In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A) Bag-of-Words (BoW)
B) Word2Vec
C) Latent Dirichlet Allocation (LDA)
D) One-Hot Encoding

Solutions: