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Exam IBM C1000-185 Simulator Online - C1000-185 Visual Cert Exam

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Quiz C1000-185 - Fantastic Exam IBM watsonx Generative AI Engineer - Associate Simulator Online

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following best describes the process of large-scale iterative alignment tuning in the context of customizing LLMs with InstructLab?

A. Repeated fine-tuning of a model using reinforcement learning, focusing on aligning its outputs with human preferences across a diverse set of tasks
B. Direct training of the model on an expanded version of the dataset, without adjusting prompts or training tasks
C. A single training run of the model on a dataset to generate better predictions for a fixed number of prompts
D. Fine-tuning the model exclusively on binary classification tasks to improve its generalization on all other tasks

Answer: A

NEW QUESTION # 64
Which two of the following are key benefits of using IBM Watsonx's Tuning Studio for fine-tuning generative AI models on specific tasks? (Select two)

A. Automatically generates prompt templates based on data input
B. Automatically selects the best model architecture for the task
C. Significantly reduces the risk of overfitting without manual intervention
D. Provides automated hyperparameter search and tuning strategies
E. Offers real-time validation of tuning experiments to optimize model performance

Answer: D,E

NEW QUESTION # 65
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. Full integer quantization with 8-bit precision
C. Post-training weight clustering
D. Mixed-precision floating-point quantization

Answer: A

NEW QUESTION # 66
A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back-end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer's intent.
Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?

A. Utilize IBM watsonx's API orchestration layer to dynamically route requests to the correct system based on intent analysis.
B. Use synchronous REST APIs for all back-end systems, relying on watsonx's built-in intent recognition to manage API routing.
C. Set up IBM watsonx to queue customer interactions and process them sequentially using a single system at a time.
D. Implement a monolithic architecture where all system interactions are handled within a single service.

Answer: A

NEW QUESTION # 67
You are developing a legal research assistant that uses Retrieval-Augmented Generation (RAG) to help lawyers find relevant case law. The system must provide case law that is not only relevant to the query but also semantically similar in meaning to the legal terminology used.
Given this scenario, which of the following retrievers would be the most appropriate choice for this application?

A. A sparse retriever based on keyword matching and BM25 algorithm.
B. A dense retriever based on embeddings from a fine-tuned BERT model.
C. A rule-based retriever optimized for simple, pre-defined keyword mappings.
D. A sparse retriever that integrates exact matching of legal terms with synonym matching.

Answer: B

NEW QUESTION # 68
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