Title: Quiz 2026 IBM C1000-185: IBM watsonx Generative AI Engineer - Associate Unparall [Print This Page] Author: guslane487 Time: yesterday 08:23 Title: Quiz 2026 IBM C1000-185: IBM watsonx Generative AI Engineer - Associate Unparall BONUS!!! Download part of Actualtests4sure C1000-185 dumps for free: https://drive.google.com/open?id=1HEONT8sR8Yfs0VGrOAB0i_R5XvhIrlFs
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In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?
A. Averaging the probabilities of multiple tokens at each step to generate a sequence
B. Randomly selecting tokens from the top-k highest probability tokens at each step
C. Selecting the token with the highest probability at each step, without considering future possibilities
D. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
Answer: C
NEW QUESTION # 299
You are tasked with developing a generative AI application for customer service, using a Watsonx model to generate responses based on user queries. The dataset includes user-provided data such as past interaction histories, query types, and feedback ratings.
Which two approaches should you implement to optimize the usage of these data elements for generating highly relevant responses? (Select two)
A. Use query type as an additional input feature to ensure the model tailors responses accordingly.
B. Prioritize the use of user feedback ratings over query type for generating more personalized responses.
C. Normalize feedback ratings data by converting them into discrete categories (e.g., low, medium, high).
D. Filter out user interaction history data that is more than six months old to focus on recent patterns.
E. Apply data augmentation techniques to artificially expand the dataset with new, synthetic interaction histories.
Answer: A,C
NEW QUESTION # 300
Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?
A. Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.
B. Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.
C. Quantization primarily reduces the size of the training dataset required for an LLM.
D. Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
Answer: D
NEW QUESTION # 301
You are using IBM watsonx Prompt Lab to experiment with different versions of a prompt to generate accurate and creative responses for a customer support chatbot.
Which of the following best describes a key benefit of using Prompt Lab in the process of prompt engineering?
A. It allows users to generate AI models without the need for training data.
B. It limits the number of iterations a user can test to prevent overfitting the prompt to specific outputs.
C. It provides a real-time environment for testing and refining prompts, helping to improve response quality.
D. It automatically generates prompts based on industry-specific data without any user input.
Answer: C
NEW QUESTION # 302
You are working with IBM Watsonx to generate automated customer support responses. To ensure consistency and flexibility in responses across multiple product categories, you decide to use prompt variables.
Which of the following best describes the benefits of using prompt variables in this scenario?
A. They enable the AI to better predict the intent of the customer query, reducing the need for explicit customer input.
B. They automatically improve the accuracy of the AI's responses by allowing the system to learn from each generated prompt.
C. They allow for dynamic input fields in prompts, making it easier to tailor responses for different product categories without rewriting the entire prompt.
D. They ensure that the AI generates responses with consistent tone and personality across all prompts, regardless of product category.
Answer: C
NEW QUESTION # 303
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