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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Which of the following factors is most likely to contribute to biased outputs in a generative AI system?

A) Repeatedly using the same prompt for multiple generations.
B) The use of too many input parameters in the prompt.
C) Insufficient diversity in the training dataset used to train the model.
D) Prompt length that exceeds the model's context window.

2. You are optimizing a prompt-tuned LLM for a financial institution's automated assistant. The assistant's main tasks include responding to customer inquiries about account balances, providing detailed transaction histories, and explaining complex financial products.
Which task should be prioritized for prompt-tuning to improve the model's performance in this domain?

A) Fine-tune the model for information retrieval tasks related to customer accounts.
B) Optimize for natural language generation to improve customer engagement.
C) Focus on improving the model's ability to generate financial market forecasts.
D) Train the model to generate creative financial advice tailored to each customer.

3. You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A) Convert all text to lowercase to ensure uniformity in the dataset.
B) Perform a spelling correction on the entire dataset to remove any language inconsistencies.
C) Manually categorize each interaction and organize them into a taxonomy tree structure.
D) Tokenize the dataset before curating it and mapping it to the taxonomy tree.

4. A financial institution is deploying a generative AI model to generate loan approval recommendations based on applicant profiles, including factors like income, credit score, and employment history. The organization is concerned about ensuring that the model does not introduce bias in its recommendations, particularly related to gender and race. You have been asked to design a process to evaluate the model's inferences during deployment and mitigate any potential bias.
Which method would be most effective for evaluating the model's inferences for bias in this deployment scenario?

A) Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.
B) Implement a fairness audit, where a sample of the model's inferences is checked for disparate impact across protected groups such as gender and race.
C) Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.
D) Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.

5. You are optimizing a generative AI prompt for creative content generation, but you want to ensure that outputs with the same prompt can vary slightly across multiple runs to maintain freshness in the responses.
Which parameter should you adjust to strike a balance between random variability and consistent quality?

A) Temperature = 0.1, Random Seed unset
B) Top-p = 0.9, Temperature = 1.0, Random Seed unset
C) Set a fixed Random Seed to zero
D) Max Tokens = 300, Random Seed set to 42

Solutions: