Free IBM C1000-185 Exam Dumps Questions & Answers

In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

• A.  Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.
• B.  Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
• C.  Quantizing a model to 8-bit precision always results in a significant loss in performance, especially when working with language models or large generative AI architectures.
• D.  Post-training quantization is more resource-efficient than quantization-aware training, as it applies quantization after the model has been fully trained, eliminating the need for additional fine-tuning.
• E.  Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.

You are tasked with improving the performance of a generative AI model that generates personalized marketing emails. The client wants the model to produce more relevant and targeted emails based on user behavior while keeping token usage and computational costs low. You decide to use Tuning Studio to achieve this.
Which of the following is a key benefit of using Tuning Studio in this scenario?

• A.  It increases the model's maximum token limit, allowing for more extensive outputs without sacrificing performance.
• B.  It allows the fine-tuning of the model's hyperparameters based on the specific domain, improving relevance and reducing token generation costs.
• C.  It provides tools for manual annotation of data to improve model accuracy.
• D.  It automatically generates custom datasets for training without needing labeled data.

You are using a generative AI model in a healthcare application to generate personalized treatment recommendations based on patient data.
Which of the following scenarios represent valid concerns related to model risks when deploying the AI in this setting? (Select two)

• A.  The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
• B.  The model generates text that includes private patient information, violating data privacy regulations.
• C.  The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.
• D.  The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
• E.  The model fails to generate treatment recommendations for some patients due to exceeding token limits during inference.

You are preparing a dataset for fine-tuning a model to classify customer complaints by category. The dataset is imbalanced, with 70% of the data representing complaints about billing, 20% representing complaints about technical issues, and 10% representing complaints about product quality.
Which of the following actions would help address the imbalance while preparing the dataset for fine-tuning? (Select two)

• A.  Undersample the majority class (billing complaints) to balance the dataset.
• B.  Apply class weighting during model training instead of modifying the dataset itself.
• C.  Oversample the under-represented classes to ensure a balanced distribution across categories.
• D.  Leave the dataset as-is, trusting the model to handle the imbalance using its internal mechanisms.
• E.  Add more synthetic data for the minority classes by using generative techniques like GPT-3 to create realistic complaints.

A client is deploying a watsonx Generative AI solution to analyze customer feedback in real time. They require a cost-effective solution that can handle occasional traffic spikes but do not expect constant heavy loads.
What would be the most appropriate approach to minimize costs while ensuring adequate performance during traffic spikes?

• A.  Use a fixed number of on-premise servers that can handle peak traffic, but run them at a lower capacity during low-demand periods.
• B.  Deploy the model using a serverless architecture with auto-scaling to handle sudden spikes in traffic.
• C.  Deploy a cloud-based instance with manual scaling to adjust resources when traffic spikes occur.
• D.  Deploy a single powerful GPU instance, relying on its ability to handle both regular and peak traffic without scaling.