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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A developer is using a GitHub Code Retrieval API to help build a search engine that can locate relevant code snippets from public repositories. The API is designed to retrieve code based on the semantic similarity of the query (e.g., a description of what the code does) to the code itself.
What is the primary advantage of using a vector-based approach for code retrieval in this scenario?

A) It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code.
B) It speeds up retrieval by limiting the search to repositories where the user has committed code in the past.
C) It retrieves code snippets based on the semantic similarity between the query and the code, enabling the discovery of relevant code even when the query and code do not use the same keywords.
D) It provides real-time updates to the code embeddings, ensuring the latest code is always retrieved.

2. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.
B) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
C) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
D) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.

3. 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) 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.
C) Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.
D) Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.

4. You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface.
Which of the following steps should you follow to ensure the model is fine-tuned correctly and the synthetic data is used effectively?

A) Select the pre-trained model, generate synthetic data, and fine-tune the model using default parameters without further customization.
B) Select the pre-trained model, generate synthetic data, inspect the generated data for quality, and fine-tune the model by adjusting hyperparameters and training settings.
C) Directly upload synthetic data without inspecting or validating it and initiate the fine-tuning process.
D) Use synthetic data as a replacement for real-world data without cross-validation or any quality control measures.

5. You are tasked with fine-tuning a large language model (LLM) using IBM's InstructLab to improve performance for a specific customer service task. The goal is to enhance the model's ability to answer questions related to account management and customer complaints.
Which of the following actions is NOT a component of the fine-tuning process in InstructLab?

A) Selecting and preprocessing a representative dataset of customer interactions for training
B) Directly adjusting the model's architecture to increase the number of attention heads in the transformer
C) Defining specific task instructions that the model will follow during inference
D) Tuning the learning rate to prevent overfitting during the fine-tuning process

Solutions: