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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q269-Q274):
NEW QUESTION # 269
A data scientist is choosing between using hard prompts and soft prompts in a generative AI project.
Which of the following best explains why hard prompts might be more suitable for scenarios where explainability is crucial?
- A. Hard prompts are based on learned embeddings, which offer better model understanding due to their complexity.
- B. Hard prompts dynamically adjust the model's internal representations, providing more clarity in complex situations.
- C. Hard prompts reduce the model's flexibility by making the output deterministic, which enhances explainability.
- D. Hard prompts allow for a clear, human-readable set of instructions that directly guide the model's behavior.
Answer: D
NEW QUESTION # 270
You are tasked with optimizing a large language model (LLM) for deployment in a resource-constrained environment where memory usage and computational cost need to be minimized without significantly compromising model accuracy.
Which quantization technique would be the most appropriate to achieve this balance?
- A. Post-training weight clustering
- B. Post-training dynamic quantization
- C. Full integer quantization with 8-bit precision
- D. Mixed-precision floating-point quantization
Answer: B
NEW QUESTION # 271
You are tasked with improving the performance of a generative AI model used for customer service automation. The model needs to respond quickly and with high accuracy, particularly for complex queries. You have access to Tuning Studio as part of your optimization toolkit.
Which of the following is a primary benefit of using Tuning Studio to optimize the model in this scenario?
- A. It enables the creation of new datasets by generating synthetic data based on prompts.
- B. It automates the process of cleaning and preprocessing the input data before model training.
- C. It provides automated fine-tuning of the model's hyperparameters to improve performance on domain-specific tasks.
- D. It allows you to manually edit the output tokens to ensure correctness.
Answer: C
NEW QUESTION # 272
You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?
- A. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
- B. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
- C. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
- D. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
Answer: B
NEW QUESTION # 273
You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn't over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information.
What configuration best addresses this issue without allowing for excessive output?
- A. Stop Sequence = '.', Max Tokens = 500
- B. Stop Sequence = '.', Max Tokens = 150
- C. Stop Sequence = '</end>', Max Tokens = 250
- D. Stop Sequence = '.' (period followed by a space), Max Tokens = 300
Answer: C
NEW QUESTION # 274
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