EXAM C1000-185 OVERVIEW | C1000-185 EXAM DUMPS.ZIP

Exam C1000-185 Overview | C1000-185 Exam Dumps.zip

Exam C1000-185 Overview | C1000-185 Exam Dumps.zip

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Tags: Exam C1000-185 Overview, C1000-185 Exam Dumps.zip, Trusted C1000-185 Exam Resource, C1000-185 Latest Examprep, C1000-185 Test Book

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q372-Q377):

NEW QUESTION # 372
When deploying a generative AI model using IBM watsonx, which of the following actions should be prioritized to ensure high availability and minimize downtime in production environments? (Select two)

  • A. Disabling failover mechanisms to reduce deployment complexity
  • B. Setting up redundant storage for the model's dataset but not for the model itself
  • C. Deploying the model on a single cloud-based server with load balancing enabled
  • D. Implementing containerization for the model through Kubernetes
  • E. Configuring auto-scaling policies based on usage patterns and resource demand

Answer: D,E


NEW QUESTION # 373
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 # 374
You are tasked with building a question-answering system using IBM WatsonX's LLM integrated with LangChain. The system needs to retrieve relevant documents from a corpus of research papers and generate accurate, contextually aware responses.
Which of the following steps is most critical when implementing a RAG pattern in this environment?

  • A. Using WatsonX LLM to pre-process the research papers before storing them in LangChain's memory.
  • B. Fine-tuning WatsonX's LLM on the research papers before enabling retrieval through LangChain.
  • C. Setting up the vector database to store embeddings of the research papers for semantic search.
  • D. Configuring LangChain to use WatsonX's LLM for exact keyword matching during document retrieval.

Answer: C


NEW QUESTION # 375
When using few-shot prompting, which of the following is a common limitation you may encounter while working with Watsonx AI for text generation tasks?

  • A. Few-shot prompting requires the model to be retrained with additional examples.
  • B. Few-shot prompting cannot handle open-ended tasks, as it only works for structured data inputs.
  • C. Few-shot prompting is only effective for classification tasks and cannot be used for text generation.
  • D. The model may overfit to the few examples provided, leading to overly specific responses.

Answer: D


NEW QUESTION # 376
You are tasked with creating a prompt template for IBM Watsonx to generate customer support responses based on user queries. The response needs to be polite, concise, and address the issue directly.
Which of the following is the most appropriate structure for a reusable prompt template to ensure consistency across multiple queries?

  • A. "Please write a polite and professional response to the customer's query, including any relevant context or background information and focusing on the core issue."
  • B. "Generate a professional response to the customer's query, avoiding repetition and unnecessary details, while focusing on addressing the issue succinctly."
  • C. "Write a short and casual response to the customer, focusing on being friendly and engaging, regardless of the content of the query."
  • D. "Generate a detailed and formal response to the customer, focusing on providing as much information as possible, even if it's unrelated to the query."

Answer: B


NEW QUESTION # 377
......

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