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[General] Valid C1000-185 Test Preparation & Interactive C1000-185 Course

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【General】 Valid C1000-185 Test Preparation & Interactive C1000-185 Course

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q341-Q346):NEW QUESTION # 341
In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?
  • A. It reduces the time required to train models by lowering the accuracy threshold.
  • B. It provides real-time feedback on model deployment success rates.
  • C. It limits the number of model versions stored, improving system performance.
  • D. It allows for the precise allocation of computational resources to manage budget constraints.
Answer: D

NEW QUESTION # 342
Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?
  • A. Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.
  • B. Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.
  • C. Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
  • D. Enhancing the model's ability to generalize across unseen data by training it specifically on the variations included in the prompt template.
Answer: A

NEW QUESTION # 343
You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.
Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?
  • A. Gaussian Mixture Models (GMMs)
  • B. Anderson-Darling Based Synthetic Data Generation (ADS-DG)
  • C. Bootstrapping Algorithm
  • D. K-Nearest Neighbors (KNN)
Answer: B

NEW QUESTION # 344
You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?
  • A. Creating templates that provide extensive background information to give the model context.
  • B. Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.
  • C. Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.
  • D. Embedding explicit stop words within the prompt to control the length of generated outputs.
Answer: C

NEW QUESTION # 345
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 dynamic quantization
  • B. Post-training weight clustering
  • C. Mixed-precision floating-point quantization
  • D. Full integer quantization with 8-bit precision
Answer: A

NEW QUESTION # 346
......
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