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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 tasked with improving the performance of a generative AI model used for customer service automation. The model needs to respond quickly and with high accuracy, particularly for complex queries. You have access to Tuning Studio as part of your optimization toolkit.
Which of the following is a primary benefit of using Tuning Studio to optimize the model in this scenario?

A) It allows you to manually edit the output tokens to ensure correctness.
B) It provides automated fine-tuning of the model's hyperparameters to improve performance on domain-specific tasks.
C) It automates the process of cleaning and preprocessing the input data before model training.
D) It enables the creation of new datasets by generating synthetic data based on prompts.

2. A business is implementing a RAG solution to enhance its chatbot capabilities. The chatbot needs to answer queries using a large collection of unstructured documents.
Which scenario best highlights when to use a vector database to augment this system?

A) When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.
B) When performing traditional database operations like sorting and filtering based on numeric or categorical values.
C) When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.
D) When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.

3. You are developing an AI-driven application using IBM watsonx and LangChain to automate legal document summarization for a law firm. The application needs to extract key legal points, summarize them, and generate insights from various sources, including external APIs, court databases, and private document repositories. You are tasked with creating a LangChain chain that integrates these sources, customizes prompt templates, and uses Large Language Models (LLMs) to provide legal summaries. The prompt template must allow for dynamic insertion of text from external sources and adapt based on the type of legal document.
Which LangChain chain design would best meet the needs of this application?

A) Use a SequentialChain that first extracts text from external APIs and databases, processes it through custom prompt templates, and then sends the final processed text to an LLM.
B) Design a ParallelChain where the text from different sources is processed in parallel by multiple LLMs, combining the results at the end.
C) Implement a SimpleChain that retrieves the required data from external APIs and directly sends the text to the LLM without prompt templates.
D) Employ a Retrieval-Augmented Generation (RAG) Chain, where the LLM queries external knowledge sources in real-time while applying a fixed prompt template.

4. You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A) Increase memory utilization to reduce BLEU and ROUGE-L scores
B) Maximize BLEU score and reduce memory utilization
C) Reduce memory utilization and maintain BLEU and ROUGE-L scores
D) Focus on improving the ROUGE-L score while increasing memory utilization

5. In a Retrieval-Augmented Generation (RAG) system, you are tasked with generating vector embeddings for a large corpus of documents. You plan to use a pre-trained transformer-based model to generate these embeddings.
What is the most important factor to consider when choosing a pre-trained model for generating embeddings in this scenario?

A) The model should have been trained on a similar task (e.g., document retrieval), as this ensures the embeddings will be relevant for your corpus.
B) The model's size (in terms of parameters) should be minimized to reduce memory usage, even if it impacts embedding quality.
C) The model's embeddings should always be fine-tuned on your specific corpus before use, as pre-trained embeddings are too general for most tasks.
D) The model should generate embeddings based on sentence-level inputs only, as document-level embeddings are always too large for effective retrieval.

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