Analyze and Design a Generative AI Solution
Free IBM Certified watsonx Generative AI Engineer - Associate practice — 6 questions on Analyze and Design a Generative AI Solution, with explanations. No sign-up. Full 12-question mixed test →
A financial services company must deploy a generative AI solution for internal document summarization. Regulatory requirements mandate all data and model processing remain within a private on-premises environment, and the security team has strict compute budget constraints favoring smaller models. Which approach BEST meets these requirements?
watsonx.ai can be deployed on Cloud Pak for Data in a fully on-premises environment, satisfying data residency requirements, while a smaller Granite model keeps compute and licensing costs within budget while still delivering strong summarization quality.
An organization's product catalog changes daily, and the AI assistant must always reflect the latest data without retraining. Which architecture is the BEST design choice?
RAG retrieves the most current catalog data from the vector index at inference time, so the assistant always reflects the latest information without any retraining cycle.
A team has only 300 labeled examples for a classification task and needs to adapt a foundation model's behavior in watsonx.ai Tuning Studio. Which tuning method should they choose to avoid overfitting and control compute cost?
Prompt tuning updates only a small set of soft-prompt parameters rather than the full model, making it well suited to small labeled datasets, reducing overfitting risk and compute cost compared to full fine-tuning.
When designing a RAG pipeline, why is a foundation model's maximum context window length a critical constraint on chunk size and retrieval count?
The context window is a hard token limit covering the query, all retrieved chunks, and the generated response combined; exceeding it causes truncation or inference failure, so chunk size and retrieval count (top-k) must be sized to fit within it.
A customer support solution must call an external inventory API, retrieve results, and then generate a natural language response using watsonx.ai. Which architectural pattern BEST supports this requirement?
Agent-based orchestration allows the foundation model to decide when to invoke an external tool or API, incorporate the live returned data, and then synthesize a coherent natural language response, which is required for dynamic external API interaction.
A company needs to implement a RAG solution supporting English, Spanish, and Japanese documents in a single retrieval index within watsonx.ai. Which selection criterion is MOST important when choosing an embedding model for this design?
Retrieval quality across languages depends on the embedding model's ability to represent semantic similarity consistently across English, Spanish, and Japanese; without multilingual training, cross-language retrieval accuracy degrades significantly.
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