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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q300-Q305):
NEW QUESTION # 300
When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?
- A. Focus on generating prompts specific to a single domain to train the model on specialized tasks.
- B. Prioritize prompts with repetitive patterns to help the model memorize key responses.
- C. Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.
- D. Use a diverse set of prompts covering multiple task domains with varying levels of complexity.
Answer: D
NEW QUESTION # 301
While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?
- A. Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
- B. Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.
- C. Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.
- D. Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.
Answer: C
NEW QUESTION # 302
You are tasked with integrating IBM watsonx with an existing enterprise application that uses a custom-trained Large Language Model (LLM) to answer complex customer queries. The enterprise application requires real-time responses from the LLM, and the integration must allow for scalable, low-latency interactions across multiple customer channels, such as email and live chat. You need to ensure that the data flowing into the LLM is preprocessed appropriately and that the orchestration between different Watson services and the LLM is efficient.
What is the best approach for integrating IBM watsonx to meet these requirements?
- A. Employ IBM watsonx's Data Refinery tool to preprocess incoming data from each channel and orchestrate data flow through Apache Kafka for real-time processing.
- B. Integrate IBM watsonx Assistant to handle multi-channel inputs and orchestrate LLM responses, while using IBM Event Streams to handle real-time scalability across channels.
- C. Use IBM watsonx's Generative AI API and directly integrate it with the application via REST, ensuring the LLM receives real-time data from each channel.
- D. Directly implement IBM watsonx Machine Learning models into each communication channel to ensure low-latency interactions with the LLM.
Answer: B
NEW QUESTION # 303
In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?
- A. When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
- B. When the model needs to generate a strictly factual output with minimal deviation from the prompt.
- C. When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
- D. When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
Answer: D
NEW QUESTION # 304
You are working on a project that requires generating a large volume of product descriptions for an e-commerce website. The descriptions must be unique, creative, and optimized for SEO. The client has specified that the descriptions must also include certain technical product specifications, but they should not be overly mechanical or robotic in tone. Your team has access to an LLM pre-trained on large-scale, general-purpose corpora.
Based on this scenario, what would be your first step to design the most effective Generative AI solution for this task?
- A. Optimize the generation process using greedy decoding to ensure concise and accurate descriptions.
- B. Use prompt engineering to add technical specifications dynamically to generated text without fine-tuning the model.
- C. Fine-tune the pre-trained LLM on a domain-specific dataset of e-commerce product descriptions with an emphasis on SEO-friendly language.
- D. Use the pre-trained LLM directly without any modification and feed it the product specifications, prompting it to generate descriptions.
Answer: C
NEW QUESTION # 305
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