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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)

A) Increase the number of training epochs
B) Enable early stopping
C) Reduce the learning rate
D) Decrease the batch size
E) Increase the model's complexity by adding more layers

2. You are fine-tuning a generative AI model in IBM Watsonx and need to define appropriate stopping criteria to ensure the generated text is relevant and coherent.
Which of the following would be an example of a valid stopping criterion for a text generation task?

A) The model will stop generating text once a predefined number of characters is reached, ensuring that the output does not exceed a set length.
B) The model will stop generating text once the average probability of each word in the output falls below a 50% threshold.
C) The model will stop generating text when it encounters a special end-of-sequence token or punctuation such as a period or question mark.
D) The model will stop generating text once it detects a repetitive sequence of words or phrases, automatically cutting off redundancy.

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

4. You are building a customer support chatbot using IBM watsonx.ai and Watson Assistant. The chatbot must use watsonx.ai's large language model (LLM) to generate dynamic responses and Watson Assistant to manage dialog and interaction flow.
What is the most efficient way to integrate these two services to deliver an optimal solution?

A) Use Watson Assistant as the primary interface and call watsonx.ai's LLM through an API for generating dynamic responses in specific intents.
B) Build a separate microservice for each service, allowing Watson Assistant and watsonx.ai's LLM to operate independently, with no communication between them.
C) Deploy watsonx.ai's LLM within Watson Assistant by embedding the LLM directly into the Watson Assistant environment.
D) Use Watson Assistant to directly generate all responses, bypassing watsonx.ai's LLM.

5. While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A) Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
B) Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.
C) Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.
D) Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.

Solutions: