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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 create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.
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 both retrieve and summarize the documents, bypassing Watson Discovery.

2. You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A) Set the number of epochs equal to the size of the dataset
B) Set a high number of epochs and use early stopping to determine the optimal point
C) Gradually increase the number of epochs until the loss on the training set reaches zero
D) Use a single epoch to avoid overfitting

3. You are working with IBM Watsonx to generate automated customer support responses. To ensure consistency and flexibility in responses across multiple product categories, you decide to use prompt variables.
Which of the following best describes the benefits of using prompt variables in this scenario?

A) They ensure that the AI generates responses with consistent tone and personality across all prompts, regardless of product category.
B) They allow for dynamic input fields in prompts, making it easier to tailor responses for different product categories without rewriting the entire prompt.
C) They enable the AI to better predict the intent of the customer query, reducing the need for explicit customer input.
D) They automatically improve the accuracy of the AI's responses by allowing the system to learn from each generated prompt.

4. In the context of generative AI and large language models, text embeddings are a key component.
What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?

A) Text embeddings are used to reduce the dimensionality of the input text for faster training of the generative model.
B) Text embeddings are used to randomly assign values to words in the corpus, providing variability in how the model generates text.
C) Text embeddings generate a one-to-one mapping of words, allowing for the exact reconstruction of the original input text.
D) Text embeddings map words and phrases to high-dimensional vectors that capture their semantic meaning, allowing for efficient document retrieval based on contextual similarity.

5. You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?

A) Embeddings store the entire content of documents, which is then directly passed to the generative model.
B) Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
C) Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.
D) Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.

Solutions: