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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q90-Q95):

NEW QUESTION # 90
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. Fine-tune the model for information retrieval tasks related to customer accounts.
B. Focus on improving the model's ability to generate financial market forecasts.
C. Train the model to generate creative financial advice tailored to each customer.
D. Optimize for natural language generation to improve customer engagement.

Answer: A

NEW QUESTION # 91
You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

A. Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
B. Customize prompt templates to closely mimic the structure and format of the original training data.
C. Choose a diverse set of prompts that span different domains unrelated to the original dataset.
D. Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.

Answer: B

NEW QUESTION # 92
You are tasked with integrating a generative AI model on watsonx.ai into a custom business workflow. The workflow requires complex prompt chains and interaction with external APIs.
Which of the following best describes how you should approach the integration using watsonx.ai and LangChain?

A. Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.
B. Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.
C. Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.
D. Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.

Answer: C

NEW QUESTION # 93
While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A. Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
B. Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
C. Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.
D. Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.

Answer: C

NEW QUESTION # 94
You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A. "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."
B. "Generate a summary for [New Contract]."
C. "Example 1: [First Contract]
D. "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."

Answer: A

NEW QUESTION # 95
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