IBM watsonx Generative AI Engineer - Associate : C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

A. A soft prompt is a fixed string of text used in fine-tuning, while a hard prompt adjusts dynamically based on input data.
B. A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.
C. Hard prompts are less efficient because they need to be re-trained with each task, while soft prompts are more versatile and adaptive across multiple tasks.
D. Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.

Question 2

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. Implement a SimpleChain that retrieves the required data from external APIs and directly sends the text to the LLM without prompt templates.
C. Design a ParallelChain where the text from different sources is processed in parallel by multiple LLMs, combining the results at the end.
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 3

You are fine-tuning the output behavior of a generative AI model in IBM Watsonx for creative content generation. You decide to adjust the temperature parameter to influence the randomness of the model's output.
Which of the following best describes the effect of increasing the temperature value?

A. Increasing the temperature makes the model generate more deterministic responses by always selecting the most probable token at each step.
B. A higher temperature setting reduces the length of the generated output by limiting the number of tokens in each response.
C. Raising the temperature encourages the model to consider less likely tokens, leading to more diverse and creative outputs.
D. Raising the temperature makes the model more likely to repeat tokens, reducing variability in its responses.

Question 4

You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

A. Increase the model's temperature to allow for more diverse outputs.
B. Use beam search with a high beam width to generate the most likely sequence.
C. Reduce the maximum token limit and set a higher repetition penalty.
D. Implement a post-processing step that filters for factual accuracy using a knowledge base.

Question 5

You are optimizing a Generative AI model for a business application where cost savings are a priority.
Which of the following modifications to the model's parameters will most effectively reduce the overall generation cost while minimizing the loss of output quality?

A. Set a lower value for the frequency penalty parameter.
B. Reduce the number of layers in the neural network during inference.
C. Set a lower value for the top-p (nucleus sampling) parameter.
D. Decrease the model's context window size.

Solutions: