IBM IBM Certified watsonx Generative AI Engineer - Associate C1000-185

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In a Retrieval-Augmented Generation (RAG) setup, you notice that the model is generating responses that are not always relevant to the query, despite the knowledge base containing useful information.
What could be the most likely cause of this issue, and how should you address it?

A) The model is over-relying on the retrieval system and ignoring the language model's ability to generate coherent responses, so you should disable the retrieval component for general questions.
B) The knowledge base might contain outdated or irrelevant documents, so removing all non-recent documents would ensure the model generates more relevant responses.
C) The problem likely lies with the input format, so changing all queries to a pre-structured format (like templates) will ensure the retrieval and generation stages perform optimally.
D) The retrieval mechanism might be failing to fetch the most relevant documents from the knowledge base, so you should improve the search algorithm or use a better ranking system.

2. In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

A) Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
B) Greedy decoding works best when combined with temperature scaling to increase randomness
C) Greedy decoding is highly effective when multiple equally probable tokens are available at each step
D) Greedy decoding guarantees the highest overall probability for the output sequence

3. You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?

A) The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.
B) The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
C) The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.
D) The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.

4. You are working on a Retrieval-Augmented Generation (RAG) system where large-scale document retrieval is a critical component. To improve the efficiency and accuracy of retrieval, you need to store and query vector embeddings. Given that the system needs to handle billions of high-dimensional embeddings while maintaining low latency for search queries, you are evaluating the use of a vector database.
Which of the following databases would be the most appropriate choice for this purpose, and why?

A) A graph database like Neo4j, which is designed for traversing relationships between data points.
B) A document-based NoSQL database like MongoDB, utilizing full-text search capabilities.
C) Relational databases with B-tree indexes.
D) A vector database like Pinecone or Weaviate that supports approximate nearest neighbor (ANN) search.

5. When tuning the generative model parameters, which of the following scenarios describes an appropriate use of the maximum tokens setting, and how will it influence the model's output?

A) Setting the maximum tokens to 500 will guarantee that the model always generates exactly 500 tokens
B) Setting the maximum tokens to 100 will prevent the model from generating coherent sentences, as it cuts off abruptly after 100 tokens
C) Setting the maximum tokens to 100 ensures that the model's output will not exceed 100 tokens, but it may stop earlier if the generation ends naturally
D) Setting the maximum tokens to 0 will allow the model to generate an unlimited amount of text

Solutions: