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IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

2. You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

A) Generate only a small amount of synthetic data to minimize computational costs and training time.
B) Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
C) Ensure that the synthetic data covers edge cases as well as common industry scenarios.
D) Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
E) Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.

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

4. Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A) Use adversarial training techniques to make the model more robust against bias and poisoning.
B) Apply prompt engineering techniques to avoid triggering known biases in the model.
C) Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.
D) Fine-tune the model using only the training data from trusted sources, without expanding the dataset.

5. After tuning a generative AI model to produce more concise legal document summaries, you notice that while the summaries are accurate, they tend to be overly verbose. The tuning report shows that the model's perplexity is relatively high, suggesting that it is struggling with token prediction uncertainty, possibly due to an overly complex output format.
Which of the following tuning parameters would you most likely adjust to address the verbosity issue without reducing accuracy?

A) Decrease the temperature
B) Decrease the learning rate
C) Increase the number of epochs
D) Increase the maximum token length

Solutions: