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C1000-185 Question Explanations, Valid C1000-185 Exam Labs

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q180-Q185):

NEW QUESTION # 180
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. Containerize each AI asset separately, deploy them as microservices on a cloud platform, and use load balancing to manage varying demand across regions.
B. Fine-tune the pretrained model for each client individually, deploy as separate models for each use case, and store them locally for each region.
C. 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.
D. Deploy the AI assets on edge devices near the customer, reducing latency and ensuring consistent performance without relying on cloud infrastructure.

Answer: A

NEW QUESTION # 181
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 automatically increases the number of layers for more complex tasks.
B. It allows incremental training, saving computational resources by reusing checkpoints.
C. It expands the model's architecture to handle larger datasets.
D. It optimizes the model for multilingual capabilities by adding new embeddings.

Answer: B

NEW QUESTION # 182
IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.
Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?

A. It significantly reduces the computational costs associated with model fine-tuning by only updating the model's parameters relevant to the specific task, preserving the general knowledge of the base model.
B. It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.
C. It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
D. It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.

Answer: A

NEW QUESTION # 183
Your team is tasked with enhancing a document search engine using IBM watsonx Discovery. The search engine will be used to help employees quickly find relevant internal documentation, such as policy guides, project specifications, and technical manuals. You have decided to use Retrieval-Augmented Generation (RAG) to enhance this search engine's performance. The current focus is on selecting the best retriever for the task and setting up a vector database to store document embeddings.
Which of the following retriever types is most suitable for this RAG-based search engine setup, considering the diverse and unstructured nature of the documents, and why?

A. Hybrid retriever that combines sparse retrieval methods with dense embeddings stored in a flat-file system.
B. Sparse retriever with BM25 ranking in combination with a traditional relational database.
C. Rule-based retriever using manually crafted rules to find documents based on predefined logic.
D. Sparse retriever with BM25 ranking in combination with a traditional relational database.

Answer: D

NEW QUESTION # 184
When tuning the generative model parameters, which of the following scenarios describes an appropriate use of the maximum tokens setting, and how will it influence the model's output?

A. Setting the maximum tokens to 100 will prevent the model from generating coherent sentences, as it cuts off abruptly after 100 tokens
B. Setting the maximum tokens to 100 ensures that the model's output will not exceed 100 tokens, but it may stop earlier if the generation ends naturally
C. Setting the maximum tokens to 0 will allow the model to generate an unlimited amount of text
D. Setting the maximum tokens to 500 will guarantee that the model always generates exactly 500 tokens

Answer: B

NEW QUESTION # 185
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