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最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題:

1. You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.
Under which of the following circumstances is the use of a vector database most appropriate?

A) When the text corpus consists entirely of predefined categories that can be handled by simple keyword matching algorithms
B) When the corpus consists mainly of short, structured text like JSON records and traditional SQL indexing will suffice
C) When the data consists primarily of binary files such as images and videos, and full-text search is required
D) When real-time similarity search over high-dimensional embeddings is needed for large-scale unstructured text data

2. Which of the following best describes the process of large-scale iterative alignment tuning in the context of customizing LLMs with InstructLab?

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

3. You are tasked with optimizing a generative AI model in IBM watsonx.ai for an NLP-based application.
During the planning stage, which of the following data elements is most important to ensure the model generalizes well to real-world application usage?

A) A large, diverse dataset with both structured and unstructured data
B) High-dimensional feature vectors from a small, well-curated dataset
C) Pre-processed data with stop words removed to reduce noise
D) Only the structured data relevant to specific business rules

4. You are tasked with designing a LangChain-based AI workflow using watsonx.ai that incorporates multiple models for different tasks: document classification, entity extraction, and text generation. The final output should consist of a well-structured report that combines these processes.
What is the best strategy to orchestrate this workflow to ensure seamless integration of all tasks and a coherent final output?

A) Use document classification to filter the input data, followed by entity extraction to identify key terms, and then pass the refined information to watsonx.ai for text generation.
B) Run the document classification, entity extraction, and text generation tasks in parallel and merge the results at the end to produce the final report.
C) Start with text generation using watsonx.ai and then refine the output through document classification and entity extraction.
D) Build a modular workflow where each task operates independently without passing data between them, ensuring each task can be validated separately before generating the final report.

5. 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 selects the best model architecture for the task
B) Automatically generates prompt templates based on data input
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

問題與答案：