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IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

2. In a RAG system, the retriever is responsible for fetching relevant documents or information from a knowledge base based on the input query. Different retriever types can be used depending on the nature of the task.
Which retriever type is most suitable for a RAG system that requires efficient large-scale retrieval from a document corpus based on semantic similarity?

A) Exact-Match Retriever: Returns documents based solely on keyword matching and is optimized for highly structured, labeled datasets.
B) Lexical Retriever: Primarily returns results based on syntactic similarity, relying on word order and surface-level features of the input query.
C) Hybrid Retriever: Combines syntactic retrieval methods (like BM25) with semantic retrieval (like dense retrieval) but sacrifices retrieval speed for accuracy.
D) Dense Retriever: Uses vector embeddings to retrieve documents based on the semantic similarity of the input query and stored documents.

3. You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

A) Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.
B) Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.
C) Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.
D) Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.

4. You are tasked with securing an endpoint for a generative AI model that interacts with external applications.
Which of the following practices is MOST effective in ensuring both security and stability of the model's endpoint in a production environment?

A) Allow all external applications unrestricted access to the endpoint to facilitate wider model usage.
B) Implement strict IP whitelisting and authentication, but allow unfiltered prompts for flexibility.
C) Focus exclusively on endpoint authentication without enforcing any rate limits to prevent user frustration.
D) Limit the number of API calls per minute, restrict prompt lengths, and apply input validation to prevent malicious queries.

5. You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

A) Increase the temperature parameter to introduce more diversity and creativity into the model's output.
B) Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
C) Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
D) Set the minimum token length high to ensure the model has enough time to fully develop its response.

Solutions: