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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

A) Soft prompts offer higher flexibility by dynamically adjusting the model's behavior based on pre-learned embeddings rather than relying on static instructions.
B) Soft prompts are preferable in scenarios where the task requires rigid structure and adherence to specific predefined guidelines.
C) Soft prompts allow for more human oversight by embedding specific instructions into the model's generation process.
D) Soft prompts produce more deterministic outputs because they are optimized for a specific task through manual input adjustment.

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

3. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A) Top-k sampling selects the token with the highest probability, ignoring all other token options.
B) Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.
C) Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
D) Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.

4. You are optimizing a large language model (LLM) by prompt-tuning it for specific enterprise-level tasks. The goal is to initialize the prompt in such a way that it helps the model generalize well across various enterprise domains, such as finance, healthcare, and retail.
What is the most effective method to initialize the prompt for such a use case?

A) Use a short prompt that provides no guidance and allow the model to self-optimize
B) Start with a general prompt and gradually specialize it during fine-tuning
C) Use a single, highly specific prompt tailored to only one domain, such as finance
D) Initialize the prompt with an ensemble of prompts covering multiple domains

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

Solutions: