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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?

A) It reduces the overall size of the dataset by filtering out irrelevant data.
B) It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
C) It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
D) It ensures that the model only focuses on specific medical specialties by eliminating other categories.

2. You are optimizing a generative AI model using prompts. You are tasked with choosing between a hard prompt and a soft prompt for generating a technical report.
Which option best describes a soft prompt in this context?

A) A prompt that directly instructs the model with domain-specific keywords and structured language to steer the model's output.
B) A manually written prompt that is optimized using a grid search of the best keywords and patterns for the task.
C) A prompt that restricts the model's output using hard-coded rules to ensure specific behavior during generation.
D) A prompt that influences the model by embedding learned vectors into the input, modifying the generation behavior indirectly.

3. You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai's large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.
What is the best approach to achieve this integration?

A) Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.
B) Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.
C) Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.
D) Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.

4. A company is using a Retrieval-Augmented Generation (RAG) system to enhance its question-answering model by incorporating relevant documents from a knowledge base. They need to generate vector embeddings for these documents and the queries using a pretrained transformer-based model.
What is the most crucial aspect of generating vector embeddings for effective retrieval in this scenario?

A) Generate vector embeddings with a static embedding model that does not account for fine-tuning on the specific dataset.
B) Use a different transformer model for document embeddings and query embeddings to ensure diversity in representations.
C) Ensure that both documents and queries are encoded using the same embedding model.

5. How can developers best ensure that prompt templates are version-controlled and any changes to the templates can be tracked efficiently in a collaborative team environment?

A) Implement a versioning system for prompt templates in a source control tool like Git
B) Use a centralized cloud storage system that automatically saves all versions of the prompt templates
C) Manually keep a changelog of prompt template modifications in a shared document
D) Update prompt templates directly in production and rely on error logs for tracking changes

Solutions: