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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

2. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A) Top-k sampling selects the token with the highest probability, ignoring all other token options.
B) Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
C) Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
D) Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.

3. You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?

A) Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
B) Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
C) Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
D) Fine-tuning the generative model on the entire document corpus without retrieval components.

4. You are tasked with setting up a pipeline that uses an embedding API for a RAG system. Before the API can be used to generate vector embeddings for large-scale document retrieval, certain prerequisites must be met.
Which of the following is a valid prerequisite for using the embedding API efficiently?

A) Ensuring that all data sources are available in a structured format such as JSON or CSV
B) Precomputing the embeddings manually to avoid real-time API calls
C) Training the API to generate embeddings for every possible word in the corpus
D) Securing the API access with proper authentication and authorization measures

5. Condition-based prompts, where specific actions are taken depending on input patterns, are part of advanced prompt design, allowing developers to create more context-aware interactions.

A) The top-k sampling parameter controls how many potential next words are considered during each generation step, limiting the randomness of the output.
B) The temperature parameter controls the length of the generated output by increasing or decreasing the model's word count limit.
C) The learning rate parameter adjusts the creativity of the model's outputs by encouraging the model to explore more diverse topics.
D) The greedy decoding parameter improves output diversity by ensuring that the most likely token is always chosen at each step in the generation process.

Solutions: