IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A. Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
B. Include fairness metrics in the model evaluation stage to monitor for biased outputs.
C. Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
D. Use a comprehensive training dataset that includes diverse business domains to reduce biases.

Question 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. Training the API to generate embeddings for every possible word in the corpus
D. Securing the API access with proper authentication and authorization measures

Question 3

You are tasked with generating reproducible and consistent results for a particular GenAI model prompt during development and testing.
Which of the following is the primary model parameter to adjust in order to ensure that identical inputs produce identical outputs every time the model is run?

A. Top-p (Nucleus Sampling)
B. Random Seed
C. Max Tokens
D. Temperature

Question 4

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

Question 5

A data scientist is choosing between using hard prompts and soft prompts in a generative AI project.
Which of the following best explains why hard prompts might be more suitable for scenarios where explainability is crucial?

A. Hard prompts allow for a clear, human-readable set of instructions that directly guide the model's behavior.
B. Hard prompts reduce the model's flexibility by making the output deterministic, which enhances explainability.
C. Hard prompts dynamically adjust the model's internal representations, providing more clarity in complex situations.
D. Hard prompts are based on learned embeddings, which offer better model understanding due to their complexity.

Solutions: