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IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

2. You are tasked with designing a Retrieval-Augmented Generation (RAG) system using embeddings to improve the response quality of a generative AI model.
In this context, what are embeddings used for, and how do they contribute to enhancing the generative AI's performance?

A) Embeddings transform the input data into high-dimensional vectors, capturing semantic similarities between the input query and potential retrieval candidates to provide contextually relevant information for the generative model.
B) Embeddings serve as a form of knowledge storage within the generative model, allowing it to answer questions without retrieving external information.
C) Embeddings provide a summary of the input data, which the model then uses to generate its final output without retrieving external content.
D) Embeddings compress the input data to reduce computational load, improving the efficiency of the retrieval and generation process.

3. When analyzing the results of a prompt tuning experiment, which two of the following actions are most appropriate if you observe a consistently high variance in model predictions across different prompt templates? (Select two)

A) Enable regularization techniques like dropout
B) Add more layers to the model to increase complexity
C) Increase the number of training samples used for tuning
D) Increase the batch size during training
E) Tune the prompt templates further by standardizing the structure

4. You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?

A) Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
B) Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
C) Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
D) Fine-tuning the generative model on the entire document corpus without retrieval components.

5. You are deploying a Generative AI solution for a client who needs to generate customer service emails in multiple languages. The client has provided a dataset of historical customer service emails, and they want to ensure that their generative model consistently produces accurate, contextually appropriate responses across different languages. The client also has concerns about the latency of the model's responses. Based on these requirements, you are tasked with planning the deployment of the generative AI solution.
Which deployment strategy would be most appropriate for this client's needs, considering latency, language handling, and response quality?

A) Use a fine-tuned model per language, with nucleus sampling and run them on a single GPU instance to reduce the infrastructure cost.
B) Deploy a tuned prompt for each language on separate models, using greedy decoding to minimize latency, and scale across multiple GPUs for each language.
C) Deploy a single multilingual model with tuned prompts for each language, using top-k sampling and leveraging a multi-GPU distributed environment to balance response time and quality.
D) Deploy a single multilingual model using beam search decoding, and run the model on a single GPU cluster to ensure consistent responses.

Solutions: