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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with integrating a generative AI model on watsonx.ai into a custom business workflow. The workflow requires complex prompt chains and interaction with external APIs.
Which of the following best describes how you should approach the integration using watsonx.ai and LangChain?

A) Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.
B) Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.
C) Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.
D) Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.

2. Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A) To generate new responses based on training data, without relying on external data sources.
B) To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
C) To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.
D) To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.

3. You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)

A) Normalize the vector embeddings after generation to ensure they are comparable during retrieval.
B) Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
C) Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.
D) Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
E) Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.

4. In a Retrieval-Augmented Generation (RAG) system, you are tasked with generating vector embeddings for a large corpus of documents. You plan to use a pre-trained transformer-based model to generate these embeddings.
What is the most important factor to consider when choosing a pre-trained model for generating embeddings in this scenario?

A) The model should have been trained on a similar task (e.g., document retrieval), as this ensures the embeddings will be relevant for your corpus.
B) The model's size (in terms of parameters) should be minimized to reduce memory usage, even if it impacts embedding quality.
C) The model's embeddings should always be fine-tuned on your specific corpus before use, as pre-trained embeddings are too general for most tasks.
D) The model should generate embeddings based on sentence-level inputs only, as document-level embeddings are always too large for effective retrieval.

5. You are deploying a new version of a generative AI model in IBM Watsonx, and you want to maintain the integrity of prompt versioning throughout the deployment lifecycle.
Which of the following methods is the most effective for ensuring that the correct prompt version is used with the corresponding model version in production?

A) Implement model registry tags that associate a specific prompt version with each model version during deployment.
B) Use semantic versioning for both the model and the associated prompts, and track them independently in separate systems.
C) Hard-code the prompt within the model deployment script to ensure that the correct prompt is always used
D) Use the latest available prompt version for every deployment, without specifying an exact version

Solutions: