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C1000-185 Free Pdf Guide - Reliable C1000-185 Exam Answers

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q92-Q97):

NEW QUESTION # 92
When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A. It expands the model's architecture to handle larger datasets.
B. It automatically increases the number of layers for more complex tasks.
C. It allows incremental training, saving computational resources by reusing checkpoints.
D. It optimizes the model for multilingual capabilities by adding new embeddings.

Answer: C

NEW QUESTION # 93
You are designing a prompt template for generating personalized marketing emails using IBM Watsonx. The emails need to be engaging, personalized based on customer data, and must include a clear call to action.
Which of the following is the best structure for a prompt template that can be reused to generate such emails?

A. "Create a marketing email that lists the features of our latest products. No need to include any personalized information."
B. "Generate a generic marketing email promoting our products. Make it professional but avoid using customer-specific details."
C. "Generate a formal email to promote the latest offers, focusing on the technical details of the products and avoiding any emotional appeal."
D. "Write an email promoting the following product, ensuring to highlight customer-specific preferences and recommend personalized products or offers. Include a clear call to action."

Answer: D

NEW QUESTION # 94
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom-trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs.
Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?

A. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
B. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.
C. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
D. Deploy all AI assets as a single monolithic model on a multi-GPU setup, allowing scalability across multiple customers without the need for further customization.

Answer: C

NEW QUESTION # 95
Your team is building a natural language processing pipeline using IBM watsonx components, where data from multiple external APIs and user inputs needs to be transformed, analyzed, and routed through various AI models. The process should involve the dynamic selection of models based on input data characteristics. The goal is to minimize latency while maintaining accuracy across tasks like sentiment analysis, text summarization, and query generation.
Which IBM watsonx service would you use to implement a flexible, model orchestration pipeline that meets these requirements, and why?

A. IBM watsonx Data Refinery, as it can preprocess and analyze incoming data, and use its rules- based engine to route it to different models.
B. IBM watsonx API Gateway to handle external data inputs, route them to different models, and ensure that each input is preprocessed in a low-latency manner.
C. IBM watsonx Model Management to dynamically select and orchestrate the models for different tasks based on real-time data analysis.
D. IBM watsonx Orchestrator, which allows for the integration and management of multiple AI models and can dynamically route inputs to the appropriate model based on predefined criteria.

Answer: D

NEW QUESTION # 96
Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A. To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.
B. To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
C. To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.
D. To generate new responses based on training data, without relying on external data sources.

Answer: B

NEW QUESTION # 97
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