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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are testing a new version of a prompt template designed to improve the accuracy of responses from a generative model deployed on IBM Watsonx. After deploying the new prompt version, you need to ensure that it performs better or at least as well as the previous version.
Which of the following approaches provides the most reliable method for testing the performance of the new prompt template version?

A) Test the new prompt in production without monitoring and observe user feedback to gauge performance.
B) Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.
C) Run a series of A/B tests comparing the new prompt template to the old one, using a set of predetermined metrics, such as response accuracy and completion time.
D) Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.

2. In a Retrieval-Augmented Generation (RAG) system designed for technical document retrieval, you are tasked with implementing text chunking techniques using the LangChain library. The technical documents are large and contain numerous tables, figures, and bullet points.
What is the most effective way to handle text splitting to ensure high-quality retrieval?

A) Convert tables and figures into plain text and split the document by character count to maintain even chunk sizes.
B) Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.
C) Use a hybrid approach, splitting the text by both semantic boundaries (like paragraphs) and content-specific markers (like bullet points and tables), while keeping chunks within the model's token limit.
D) Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.

3. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.
Which of the following strategies would most effectively reduce bias in the model' responses?

A) Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.
B) Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.
C) Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.
D) Apply a post-processing filter that removes any gendered language after the model generates the response.

4. You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

A) Implementing zero-shot learning techniques
B) Providing a list of credible sources in the prompt
C) Using more open-ended prompts
D) Increasing the model's temperature parameter

5. Which of the following practices are best suited to optimize the performance of a deployed generative AI model in IBM watsonx under real-world traffic conditions? (Select two)

A) Loading the entire model into memory at runtime to avoid latency issues
B) Employing model quantization techniques during deployment
C) Monitoring and adjusting resource allocation dynamically based on usage statistics
D) Relying on a single model configuration across all hardware types to simplify deployment
E) Using batch processing instead of real-time inference for all requests

Solutions: