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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q228-Q233):
NEW QUESTION # 228
You are integrating watsonx.ai into an external system to handle text generation for a content creation application. The external system requires real-time processing and needs to interact with watsonx.ai frequently. Given this requirement, which integration method is most appropriate for ensuring reliable and scalable communication between the external system and watsonx.ai?
- A. Implement Webhooks to receive updates from watsonx.ai when new data is generated and push it to the external system.
- B. Integrate watsonx.ai through the SDK to directly embed AI capabilities into the external system, eliminating the need for API calls.
- C. Use a REST API with synchronous requests, where the external system waits for watsonx.ai to respond before proceeding.
- D. Leverage asynchronous REST API calls with callbacks to enable the external system to send requests and continue processing while waiting for the response.
Answer: D
NEW QUESTION # 229
You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?
- A. LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.
- B. LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
- C. LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.
- D. LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.
Answer: A
NEW QUESTION # 230
You are designing a customer support chatbot using watsonx.ai as the primary generative model. You want to enhance the chatbot's capabilities by integrating it with IBM Watson Assistant to handle structured conversations while allowing watsonx.ai to generate responses for open-ended queries.
Which integration approach would most effectively combine both services while maintaining optimal performance and accuracy?
- A. Implement Watson Assistant for structured conversations and use a middleware layer that dynamically routes complex, open-ended queries to watsonx.ai, returning the results within the same session.
- B. Create separate chat interfaces for Watson Assistant and watsonx.ai, and allow the user to choose which system to query based on their needs.
- C. Train Watson Assistant to handle both structured and unstructured conversations, while using watsonx.ai only for rare edge cases that Watson Assistant cannot manage.
- D. Use Watson Discovery to preprocess all queries before routing them to either Watson Assistant or watsonx.ai based on query complexity.
Answer: A
NEW QUESTION # 231
You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.
What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?
- A. Increase the number of epochs
- B. Apply dropout during training
- C. Increase the learning rate
- D. Reduce the size of the training dataset
Answer: B
NEW QUESTION # 232
In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?
- A. When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
- B. When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
- C. When the model needs to generate a strictly factual output with minimal deviation from the prompt.
- D. When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
Answer: D
NEW QUESTION # 233
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