4.1 Describe the Google Cloud-Recommended Steps to Successfully Implement a Transformational Gen AI Solution. GENERATIVE-AI-LEADER Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   During the integration of a gen AI solution, what does the 'data readiness' step primarily involve?

   • ADefining the business problem
   • BSelecting the cloud region for the model
   • CEnsuring that the data used for training or fine-tuning is clean, labeled, and representative
   • DDeploying the model to a production environment
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   A marketing team wants a tool that can generate multiple versions of ad copy for A/B testing. Which type of gen AI solution best matches this need?

   • ACode generation
   • BText generation
   • CPersonalized recommendation generation
   • DImage generation
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A healthcare organization wants to implement a gen AI solution to summarize patient records for clinicians. They have strict data residency requirements and need to ensure the solution integrates with existing EHR systems. They also want to measure the time saved by clinicians. What is the FIRST step they should take in the implementation process?

   • AIntegrate the gen AI API with the EHR system to start generating summaries.
   • BSelect a pre-trained model from the Model Garden that meets data residency requirements.
   • CDefine the business objective and success metrics, such as reducing documentation time by 20%.
   • DSet up a data pipeline to extract patient records from the EHR into BigQuery.
   Reveal Solution Discussion Select an answer first
5. 9foundation · easy

   Which metric is most appropriate for evaluating the business impact of a gen AI solution that automates customer support responses?

   • ALatency of the model's inference
   • BNumber of parameters in the model
   • CAverage customer resolution time
   • DModel training loss
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6. 10application · medium

   A retail company wants to generate personalized product descriptions for thousands of items. They have a large catalog with structured data (product name, features, price) and need to produce marketing copy in multiple languages. The team has no ML expertise and wants to minimize infrastructure management. Which Google Cloud solution should they choose?

   • AUse Vertex AI Gemini API with few-shot prompting to generate descriptions from the structured data.
   • BTrain a custom text generation model on Vertex AI using their catalog data.
   • CUse Cloud Vision API to analyze product images and auto-generate descriptions.
   • DDeploy an open-source language model on Compute Engine with a custom UI.
   Reveal Solution Discussion Select an answer first