You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?

• A. Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
• B. Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed
• C. Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.
• D. Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.

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

You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

• A. Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.
• B. Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.
• C. Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
• D. Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.

You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?

• A. Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
• B. Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.
• C. Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
• D. Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.

You are creating a prompt template for a generative AI model that helps technical support staff troubleshoot customer issues based on symptoms provided. The goal is to generate a clear, step-by-step diagnostic process.
What elements would improve the effectiveness of the template? (Select two)

• A. Ask the model to generate multiple diagnostic paths for each issue
• B. Include an instruction to ask clarifying questions when the issue is unclear
• C. Provide explicit instructions to break the response into steps
• D. Set a high temperature value to explore more creative diagnostic approaches
• E. Incorporate complex technical jargon to ensure expert-level responses