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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

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

Question 2

When leveraging existing data for fine-tuning an LLM in IBM watsonx, you want to optimize the model for a highly specialized domain. You also want to generate additional synthetic data to augment your dataset.
Which of the following approaches would best help you achieve your goal?

A. Manually crafting complex datasets by sampling individual instances from unrelated domains
B. Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps
C. Relying exclusively on pre-trained general models without making domain-specific modifications
D. Using unsupervised learning on your existing dataset without adding synthetic data

Question 3

When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

A. Reducing the beam size in beam search from 5 to 1
B. Decreasing the value of the temperature parameter to 0.2
C. Increasing the temperature parameter to 1.5
D. Using Top-k Sampling with a k value of 100

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

Question 5

In the context of analyzing prompt-tuning results, which statistical measure is most important to assess how well the tuned model generalizes to unseen data?

A. Accuracy on the training dataset
B. Number of epochs completed
C. Training loss
D. Validation loss

Solutions: