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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with developing a RAG system that integrates a transformer-based language model with a large document corpus. To speed up the development process, you are considering using specialized libraries designed for RAG.
Which of the following reasons best explains why these libraries are essential for your development process?

A) They allow for the retrieval of documents based purely on keyword search, optimizing for exact match over semantic similarity.
B) They provide pre-trained retrieval models and generators, eliminating the need for any fine-tuning or customization of the system.
C) They provide a graphical interface for users to build RAG systems without requiring any programming knowledge.
D) They streamline the integration of retrievers and generators, providing out-of-the-box support for embedding models and vector databases.

2. You are designing a generative AI model to generate detailed technical

A) Stop Sequence = '<END>', Max Tokens = 100
B) Max Tokens = 500: This provides enough space for a detailed technical
C) Stop Sequence = 'END', Max Tokens = 500
D) Stop Sequence = None, Max Tokens = 700

3. You are designing a Retrieval-Augmented Generation (RAG) system that will handle real-time queries from users, using a combination of a retriever and a transformer-based generator.
Which of the following implementation details is the most critical to ensure that the system delivers responses in a timely manner while maintaining accuracy?

A) The retriever and generator should operate independently of each other to avoid communication overhead.
B) The retriever should use exact match algorithms to minimize retrieval time and complexity.
C) The system should cache previous queries and responses to avoid invoking the retriever and generator multiple times.
D) The retriever should utilize an efficient vector search algorithm with approximate nearest neighbor (ANN) techniques to balance speed and accuracy.

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

5. In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

A) Training loss
B) Validation loss
C) Accuracy on the training dataset
D) Number of epochs completed

Solutions: