C1000-185試験無料問題集（380題）「IBM watsonx Generative AI Engineer

出題：1

You are deploying an AI model to a production environment using watsonx.ai. The model is a fine-tuned GPT-based generative model designed to support real-time customer interactions. The deployment must ensure scalability, security, and maintain high availability.
Which deployment strategy should you choose to best meet these requirements?

A. Use a containerized deployment with Kubernetes on a multi-node GPU cluster to support auto-scaling and fault tolerance.

B. Deploy the model on an on-premises server with an isolated environment to ensure full control and security.

C. Deploy the model on multiple virtual machines, manually balancing traffic between instances.

D. Deploy the model on a single cloud-based GPU instance, and enable scheduled maintenance to minimize downtime.

正解：A 解答を投票する

出題：2

You are fine-tuning a generative AI model using Tuning Studio for a legal document analysis task. The model needs to perform well in summarizing long, complex legal texts while minimizing the amount of computational resources used. You are tasked with optimizing the tuning process to ensure maximum efficiency and model accuracy.
Which of the following actions would most effectively optimize the tuning process in Tuning Studio for this task?

A. Reducing the number of layers in the model to decrease computational overhead.

B. Using a fixed number of training epochs to ensure consistent results across tuning iterations.

C. Adjusting the learning rate dynamically during training based on the model's real-time loss values.

D. Increasing the batch size to reduce the number of gradient updates during training, thus speeding up the process.

正解：C 解答を投票する

出題：3

You are optimizing a prompt-tuned LLM for a financial institution's automated assistant. The assistant's main tasks include responding to customer inquiries about account balances, providing detailed transaction histories, and explaining complex financial products.
Which task should be prioritized for prompt-tuning to improve the model's performance in this domain?

A. Train the model to generate creative financial advice tailored to each customer.

B. Focus on improving the model's ability to generate financial market forecasts.

C. Optimize for natural language generation to improve customer engagement.

D. Fine-tune the model for information retrieval tasks related to customer accounts.

正解：D 解答を投票する

出題：4

In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A. It provides real-time feedback on model deployment success rates.

B. It limits the number of model versions stored, improving system performance.

C. It allows for the precise allocation of computational resources to manage budget constraints.

D. It reduces the time required to train models by lowering the accuracy threshold.

正解：C 解答を投票する

出題：5

You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A. Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.

B. Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.

C. Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.

D. Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.

E. Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.

正解：B,E 解答を投票する

出題：6

In a RAG system, you need to select an appropriate retriever to fetch relevant documents from a large corpus before generating an answer. You are considering different types of retrievers, including embedding-based and keyword-based retrievers.
Which of the following describes a scenario where an embedding-based retriever using a vector database is the best choice?

A. When retrieval must rely on semantic similarity between a query and documents, even if the exact words in the query don't appear in the document

B. When documents are labeled with metadata, and only metadata needs to be searched

C. When exact keyword matching is required, and synonyms or contextual understanding are irrelevant

D. When most of the queries consist of structured queries with precise Boolean operators and relational database-style searches

正解：A 解答を投票する

出題：7

You are implementing a Retrieval-Augmented Generation (RAG) system using LangChain, IBM WatsonX, and a vector database. The system needs to answer complex technical questions by retrieving relevant technical documents and generating a coherent response.
Which of the following best describes LangChain's role in the RAG pattern implementation?

A. LangChain is responsible for storing and retrieving documents using a sparse keyword-based retriever from a traditional relational database.

B. LangChain allows WatsonX's LLM to fine-tune its parameters based on user interactions to improve future retrieval and generation accuracy.

C. LangChain optimizes document retrieval by pre-generating responses based on common queries and caching them in memory for future use.

D. LangChain serves as an intermediary, facilitating communication between the vector database and WatsonX's LLM, ensuring that retrieved documents are contextually relevant for the LLM's response generation.

正解：D 解答を投票する

出題：8

You are working on generating creative text responses using IBM watsonx's generative AI model. You need to adjust the output so that it is more diverse and creative without losing coherence.
Which of the following model parameter settings would best achieve this objective?

A. Set temperature to 1.5

B. Set temperature to 2

C. Set temperature to 0

D. Set temperature to 0.2

正解：A 解答を投票する

C1000-185試験無料問題集「IBM watsonx Generative AI Engineer - Associate 認定」