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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are using IBM's Tuning Studio to fine-tune a generative AI model for a custom text classification task. The model was pre-trained on a large corpus but shows suboptimal performance when applied to your domain-specific data. You aim to improve both accuracy and computational efficiency.
Which of the following is a primary benefit of using Tuning Studio to optimize this model?

A. Tuning Studio allows for the customization of training data at runtime without needing pre-processing.
B. Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.
C. Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.
D. Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.

Question 2

You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

A. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
B. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.
C. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
D. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.

Question 3

Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

A. Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.
B. Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.
C. Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
D. Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.

Question 4

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. 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.
C. Soft prompts are more readable and natural, whereas hard prompts consist of short, technical instructions.
D. A hard prompt explicitly specifies all constraints, while a soft prompt relies on implicit learning from continuous inputs during training.

Question 5

A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A. Apply model quantization to optimize the LLM for handling long legal documents more efficiently.
B. Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.
C. Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.
D. Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.

Solutions: