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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 rule-based retriever optimized for simple, pre-defined keyword mappings.
D) A dense retriever based on embeddings from a fine-tuned BERT model.

2. You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A) Include fairness metrics in the model evaluation stage to monitor for biased outputs.
B) Use a comprehensive training dataset that includes diverse business domains to reduce biases.
C) Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
D) Implement a post-processing filter to remove any potentially offensive or legally sensitive content.

3. You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn't over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information.
What configuration best addresses this issue without allowing for excessive output?

A) Stop Sequence = '.', Max Tokens = 500
B) Stop Sequence = '</end>', Max Tokens = 250
C) Stop Sequence = '.' (period followed by a space), Max Tokens = 300
D) Stop Sequence = '.', Max Tokens = 150

4. You are working with a foundation model pre-trained on a large general-purpose dataset, and you plan to deploy it for a specialized task in healthcare-related text generation. However, before tuning the model, you want to assess whether tuning is necessary for your use case.
Which of the following is the best indicator that it is time to tune the foundation model for your task?

A) The model's inference time is longer than expected, and you need to reduce latency for real-time applications.
B) The model's accuracy is already above 90%, but you want to achieve 95% accuracy for your task.
C) You are noticing that the model occasionally makes grammar mistakes in the generated text.
D) The model performs well on general datasets but fails to capture specific domain-related terminology and context.

5. Your team has developed multiple versions of a custom Watsonx Generative AI model to address different use cases. During deployment, the client requires that all model versions be deployed concurrently, allowing requests to be routed to the appropriate model based on the input data.
What deployment strategy would best accommodate this requirement?

A) Blue-Green deployment
B) Multi-model serving with dynamic routing
C) A/B testing deployment
D) Canary deployment

Solutions: