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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?

A) Running evaluation metrics on the baseline model to measure initial performance.
B) Defining task-specific instructions and fine-tuning them through prompt design.
C) Pre-processing and augmenting the training data to improve the model's generalization capabilities.
D) Deploying the model in a real-time environment for user feedback collection.

2. You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

A) Perplexity score
B) BLEU score
C) Token generation speed
D) Log-likelihood of generated responses

3. A team is implementing a Retrieval-Augmented Generation (RAG) system for document search and retrieval. Their goal is to enable users to retrieve contextually relevant documents from a large, unstructured text corpus. They are considering using a vector database to handle this task.
In which scenario is a vector database the most appropriate choice for storing and retrieving documents?

A) When all documents are structured and can be queried using traditional SQL queries, focusing on specific fields and categories.
B) When the system must support real-time updates and frequent data modifications, ensuring that query results always reflect the latest state of the data.
C) When exact match search is required, such as finding documents containing specific keywords or phrases.
D) When documents need to be retrieved based on semantic similarity to the user's query, even if the exact terms are not matched.

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

5. You are tasked with developing a system that uses a vector database to store embeddings generated from a large corpus of documents. The system should be able to perform fast and efficient nearest neighbor search while balancing accuracy and speed. Given the large volume of data and the need for scalability, you are considering different indexing strategies offered by vector databases.
Which of the following indexing techniques is the most appropriate for balancing search accuracy and speed in high-dimensional vector space, and why?

A) Linear search over the entire vector dataset.
B) Exact nearest neighbor (ENN) search using KD-trees.
C) Rely on full-text indexing of documents and avoid vector search altogether.
D) Approximate nearest neighbor (ANN) search using HNSW (Hierarchical Navigable Small World) graphs.

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