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IBM C1000-185 Exam Overview:

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?

A. Utilize a smaller model to minimize the likelihood of overfitting sensitive data.
B. Restrict the model's output length to reduce the risk of sensitive information leakage.
C. Increase the length of the prompts to provide more context, ensuring more accurate results.
D. Employ differential privacy techniques to add noise to the model's outputs.

Question 2

You are tasked with building a Retrieval-Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure.
How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)

A. Rely solely on word-level embeddings to capture the meaning of legal phrases and concepts.
B. Train a domain-specific embedding model using legal documents to better capture the nuances of legal terminology.
C. Apply dimensionality reduction techniques like PCA to compress embeddings and improve retrieval speed.
D. Integrate additional metadata (e.g., document date, author) into the embedding representation to improve retrieval.
E. Use an unsupervised learning approach to generate embeddings, as labeled data is not necessary for improving retrieval performance.

Question 3

You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?

A. Embedding explicit stop words within the prompt to control the length of generated outputs.
B. Creating templates that provide extensive background information to give the model context.
C. Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.
D. Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.

Question 4

You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

A. Add more domain-specific training data to improve the model's understanding of technical vocabulary.
B. Experiment with a smaller model to reduce complexity and increase inference speed.
C. Increase the learning rate for better adaptation to specialized data.
D. Use greedy decoding during inference to generate concise summaries.
E. Perform prompt engineering by providing specific task instructions for summarization.

Question 5

After prompt-tuning a generative AI model, you review its performance on multiple evaluation metrics. The metrics include accuracy, perplexity, and latency.
Which combination of these metrics would most effectively allow you to optimize the model for both user experience and content quality?

A. High accuracy, high perplexity, high latency
B. Low accuracy, high perplexity, low latency
C. High accuracy, low perplexity, low latency
D. High accuracy, low perplexity, high latency

Solutions: