1.3 Identify the Core Layers of the Gen AI Landscape and the Business Implications. GENERATIVE-AI-LEADER Practice Questions (Page 1)

Questions 1–5

1. 1expert · hard

   A research lab needs to train a custom large language model on proprietary data. They have a limited budget but require high performance and want to avoid vendor lock-in. Which infrastructure strategy best meets these constraints?

   • AUse spot/preemptible cloud GPU instances for training, with checkpointing to handle interruptions.
   • BRent a fixed number of cloud GPU instances for the entire training period.
   • CPurchase dedicated on-premises GPU servers and manage the entire stack.
   • DUse a fully managed cloud AI platform with pre-configured GPU clusters.
   Reveal Solution Discussion Select an answer first
2. 2expert · hard

   A startup is building a gen AI product and expects rapid growth in users. They have limited funding and need to keep infrastructure costs predictable. Which infrastructure choice best supports this?

   • AUse a serverless inference service that scales with demand and charges per request.
   • BSign a long-term contract for a fixed number of GPU servers to get a discount.
   • CUse a single powerful GPU server and upgrade later if needed.
   • DBuild an on-premises data center to have full control over hardware.
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   How does an AI platform contribute to a company's time-to-market for new gen AI features?

   • ABy providing pre-built components, templates, and automated pipelines that reduce manual effort
   • BBy eliminating the need for any data preparation or model evaluation
   • CBy reducing the need for infrastructure, so no compute resources are required
   • DBy guaranteeing that models will be 100% accurate without any testing
   Reveal Solution Discussion Select an answer first
5. 4expert · hard

   A company wants to deploy a gen AI chatbot for internal employee support. They have sensitive HR data and require low latency. They are considering a large foundation model via API versus a fine-tuned smaller model hosted internally. Which factor is most decisive?

   • AThe speed of deployment using an API versus setting up internal infrastructure.
   • BThe cost of API calls versus internal hosting.
   • CThe need to keep sensitive data within the company's own infrastructure.
   • DThe desire to use the most accurate model available.
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   A startup wants to build a customer-support chatbot using a foundation model. They have a small team and need to launch quickly while maintaining version control and monitoring of model performance. What should they prioritize?

   • AUsing a model hub to access pre-trained models and an MLOps platform to manage deployments and monitor performance.
   • BDirectly integrating the foundation model API into the app without any additional tooling.
   • CBuilding a custom model from scratch to have full control over the chatbot's behavior.
   • DHiring a large team of ML engineers to manually manage all model updates and deployments.
   Reveal Solution Discussion Select an answer first