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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working on tuning a generative AI model in IBM watsonx.ai for better performance in generating conversational responses. You have been asked to add new data to the project.
What is the most effective approach to adding data for model tuning?

A) Add only outlier data to improve the model's ability to handle edge cases
B) Add unstructured data with no labels for semi-supervised learning
C) Add only the test set data to ensure robust evaluation of the model
D) Add new labeled training data that closely resembles the use case scenarios

2. You have successfully deployed a custom model using IBM Watson Machine Learning. Now, an application needs to access the model for inference.
Which of the following configurations will ensure that the application has appropriate access to the deployed model?

A) Use the default IAM policies for all users in the IBM Cloud project
B) Implement OAuth2 authentication using IBM Cloud IAM for securing the API.
C) Assign the "viewer" role to the application's service account in Watson Machine Learning
D) Create a public-facing API endpoint without authentication

3. You are tasked with creating a prompt-tuned model using IBM watsonx.ai to enhance the quality of text generation for customer support. The goal is to fine-tune the model for improved context understanding based on specific customer queries.
Which of the following approaches would be the best method to initialize the prompt for tuning?

A) Construct a prompt using a large set of random tokens from the training corpus
B) Use a prompt with pre-defined output patterns to restrict the model's possible responses
C) Use a pre-trained general-purpose prompt with no domain-specific customization
D) Use a manually crafted prompt tailored to the specific context of customer support queries

4. You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?

A) Use multiple stop sequences, including a period ."
B) Use a more distinct and unlikely stop sequence, such as "<END>"
C) Increase the token limit to avoid premature cut-off
D) Set the stop sequence to a punctuation mark like ";"

5. You are working on a project where the AI model needs to generate personalized customer support responses based on various input fields like customer name, issue type, and product details. To make the system scalable and flexible, you decide to use prompt variables in your implementation.
Which of the following statements accurately describe the benefits of using prompt variables in this scenario? (Select two)

A) Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.
B) Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on-the-fly customization of responses.
C) Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.
D) Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.
E) Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.

Solutions: