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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

A) Use IBM InstructLab to generate synthetic data, but only for neutral sentiment, as the model already handles positive and negative sentiment well.
B) Fine-tune IBM InstructLab itself to generate data that closely resembles the training data format, ensuring consistent sentiment distribution.
C) Increase the diversity of synthetic data by focusing on outliers and rare sentiment cases that are underrepresented in the original dataset.
D) Use a generic prompt to generate a wide variety of data from IBM InstructLab, regardless of sentiment polarity.

2. After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

A) BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
B) F1 score; increase the training dataset size to improve overall accuracy.
C) ROUGE score; adjust the token generation limit to ensure longer outputs.
D) Perplexity score; apply additional language model fine-tuning on grammatical correctness.

3. When deploying a machine learning model in a highly regulated industry (e.g., healthcare or finance), which strategy is most effective to ensure ongoing model performance while adhering to AI governance standards?

A) Implement a model performance monitoring framework with fairness and bias detection metrics
B) Perform real-time continuous training of the model using live data from the production environment
C) Deploy the model with hard-coded rules to ensure it does not drift from expected behavior
D) Ensure model interpretability is maximized by simplifying the architecture to a linear model

4. In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?

A) Quantization always improves model performance but significantly increases model size.
B) Quantization maintains model accuracy but doubles the computation required for inference.
C) Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.
D) Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.

5. You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A) Set the temperature to 0.0
B) Increase the temperature from 0.5 to 1.5
C) Decrease the temperature from 0.8 to 0.6
D) Decrease the temperature from 0.9 to 0.3

Solutions: