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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are implementing a RAG system that uses vector embeddings to retrieve relevant information from a large corpus of documents. To store and efficiently query the vector embeddings, you need to choose a suitable vector database.
Which of the following is a key capability that a vector database must support to ensure efficient retrieval in this context?

A) Built-in support for Approximate Nearest Neighbor (ANN) search algorithms
B) The ability to store and query data only in JSON format
C) Automatic generation of vector embeddings from raw text data
D) The ability to store sparse vectors instead of dense vectors

2. You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.
To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

A) Binary quantization (1-bit)
B) 32-bit floating point quantization with fine-tuning
C) Post-training 16-bit floating point quantization
D) Post-training 8-bit integer quantization

3. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.
Which of the following strategies would most effectively reduce bias in the model' responses?

A) Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.
B) Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.
C) Apply a post-processing filter that removes any gendered language after the model generates the response.
D) Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.

4. You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)

A) Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
B) Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
C) Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.
D) Normalize the vector embeddings after generation to ensure they are comparable during retrieval.
E) Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.

5. You are tasked with generating creative text outputs using an AI language model for a marketing campaign. You want to ensure that the responses are diverse and unexpected but still somewhat relevant to the prompt.
Which combination of temperature and random seed should you use to achieve this?

A) Temperature: 0.7, Random Seed: None
B) Temperature: 1.5, Random Seed: 123
C) Temperature: 0.1, Random Seed: 42
D) Temperature: 1.0, Random Seed: 0

Solutions: