IBM C1000-185 Exam : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with setting up a pipeline that uses an embedding API for a RAG system. Before the API can be used to generate vector embeddings for large-scale document retrieval, certain prerequisites must be met.
Which of the following is a valid prerequisite for using the embedding API efficiently?

A. Training the API to generate embeddings for every possible word in the corpus
B. Securing the API access with proper authentication and authorization measures
C. Ensuring that all data sources are available in a structured format such as JSON or CSV
D. Precomputing the embeddings manually to avoid real-time API calls

Question 2

You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.
Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

A. Reduce the batch size to decrease training time and speed up generation.
B. Apply top-k sampling with k = 50 to ensure diverse and creative outputs.
C. Increase the model's learning rate to accelerate content generation.
D. Implement early stopping criteria based on token repetition to avoid lengthy generation.
E. Use beam search with a beam width of 1 to minimize computation time.

Question 3

You are using the IBM watsonx platform to fine-tune an existing generative AI model for a text-based customer support system. Due to privacy constraints, the company cannot use real customer data, so synthetic data must be generated using the platform's UI. Your task is to create and configure the synthetic data and fine-tune the model to optimize customer query handling.
Which of the following actions will best ensure the synthetic data generation process leads to a well-tuned model that handles diverse customer queries effectively? (Select two)

A. Generate a synthetic dataset that only covers the most frequent customer queries to reduce complexity in model training.
B. Leverage the user interface to create synthetic data that includes rare edge cases, such as technical support questions or multi-part inquiries.
C. Fine-tune the model immediately after generating synthetic data, without further inspection, to maintain efficiency.
D. Simulate both simple and complex customer queries, including ambiguous or vague requests, in the synthetic data.
E. Prioritize the generation of synthetically perfect customer queries with no grammatical errors to ensure high-quality data.

Question 4

What is a key advantage of using prompt variables in IBM Watsonx for a chatbot application that needs to handle multiple user intents?

A. Prompt variables allow the model to automatically detect the user's intent, ensuring accurate responses.
B. Prompt variables allow for the dynamic injection of user-specific details into the response, improving personalization.
C. Prompt variables enable developers to predefine multiple static responses for each possible user input.
D. Using prompt variables ensures that the model will always select the most relevant response based on past conversations.

Question 5

After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A. Small difference between training and validation loss
B. High training accuracy
C. Low validation loss
D. Low training loss
E. Large difference between training and validation accuracy

Solutions: