IBM watsonx Generative AI Engineer - Associate : C1000-185認證

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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 customer support chatbot for an e-commerce company using IBM watsonx and LangChain. The chatbot will interact with an external database that holds customer order history, shipping details, and product catalog data. You need to create a LangChain chain that dynamically generates responses using prompt templates tailored to customer queries, retrieves data from the external database, and incorporates LLMs to refine the answers. The goal is to provide accurate, context-aware responses to questions about order status and product details.
Which LangChain strategy will best ensure that the chatbot provides accurate, dynamic responses based on real-time customer data?

A) Implement a SimpleChain that directly connects the chatbot to the external database and generates responses from pre-defined LLM outputs.
B) Design a ParallelChain where multiple LLMs process different aspects of the customer query, such as order history and product details, combining them in the final answer.
C) Apply a MemoryChain that remembers past customer queries and uses this memory to answer future questions more accurately.
D) Use a RetrievalChain to query the external database and combine the retrieved data with a dynamic prompt template before sending it to an LLM.

2. You are deploying a generative AI model for a financial services company. The model is responsible for automating customer support and providing recommendations. Due to the sensitive nature of financial data, the company emphasizes the need for robust AI governance.
What governance mechanism should you prioritize to ensure compliance with data privacy regulations and maintain trust in AI outputs?

A) Using AI explainability techniques to make the model's decisions transparent to regulators and customers.
B) Regularly retraining the model to avoid performance degradation due to data drift.
C) Implementing role-based access control (RBAC) to restrict who can interact with the model.
D) Ensuring model version control to track changes and updates made to the model during the deployment process.

3. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
B) Lower the model's perplexity score to improve both completeness and factual accuracy.
C) Increase the length of the input prompt to ensure that responses are more complete.
D) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

4. You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately.
Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson-Darling test for normality?

A) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
B) Bootstrapping
C) Generative Adversarial Networks (GANs)
D) Decision Trees

5. In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)

A) "Recommend products"
B) "based on their interest"
C) "[customer_name]"
D) "for"
E) "[product_category]"

問題與答案：