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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. 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 sparse retriever based on keyword matching and BM25 algorithm.
C) A dense retriever based on embeddings from a fine-tuned BERT model.
D) A rule-based retriever optimized for simple, pre-defined keyword mappings.

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

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

3. A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

A) Increasing the model's response diversity by adjusting top-p sampling
B) Switching to greedy decoding for more deterministic responses
C) Using human-in-the-loop (HITL) methods for real-time validation
D) Deploying rule-based post-processing filters to validate the output

4. How can developers best ensure that prompt templates are version-controlled and any changes to the templates can be tracked efficiently in a collaborative team environment?

A) Update prompt templates directly in production and rely on error logs for tracking changes
B) Manually keep a changelog of prompt template modifications in a shared document
C) Use a centralized cloud storage system that automatically saves all versions of the prompt templates
D) Implement a versioning system for prompt templates in a source control tool like Git

5. You are working on optimizing a large language model (LLM) using quantization techniques. Your goal is to reduce memory usage while maintaining as much of the model's original accuracy as possible.
What is a common challenge faced when applying quantization to LLMs, and how can it be mitigated?

A) Quantization causes a drastic reduction in training time, but increases memory usage. Use sparse quantization to address this.
B) Quantization may cause a significant drop in model accuracy, especially in embedding layers. Using quantization-aware training can help mitigate this.
C) Embedding layers in LLMs are difficult to quantize, so it's best to skip quantization for these layers entirely.
D) LLMs are inherently resistant to quantization, so switching to a smaller model architecture is the only viable solution.

Solutions: