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CASE STUDY: ShopGlobal
Overview: Retailer, 2000 employees, $500M revenue. US-Central co-lo, Java/Tomcat monolith, Oracle RAC 20TB, batch inventory sync.
Business Req: Handle 10x Black Friday spikes, personalized recommendations, modernize to microservices.
Execs: CEO wants omnichannel; CFO needs predictable spend; CTO demands zero downtime cutover.
Tech Req: PCI-DSS compliance, automated image processing, real-time inventory, CI/CD.
Constraints: Complex Oracle stored procedures, team learning containers, strict bi-annual audits.
QUESTION:
Which compute platform should you recommend for the modernized microservices architecture, considering the team is just learning containers?
GCP PCA · Question 07 · Technical Requirements
CASE STUDY: ShopGlobal
Overview: Retailer, 2000 employees, $500M revenue. US-Central co-lo, Java/Tomcat monolith, Oracle RAC 20TB, batch inventory sync.
Business Req: Handle 10x Black Friday spikes, personalized recommendations, modernize to microservices.
Execs: CEO wants omnichannel; CFO needs predictable spend; CTO demands zero downtime cutover.
Tech Req: PCI-DSS compliance, automated image processing, real-time inventory, CI/CD.
Constraints: Complex Oracle stored procedures, team learning containers, strict bi-annual audits.
QUESTION:
How should you design the automated image processing pipeline for the product catalog?
CASE STUDY: ShopGlobal
Overview: Retailer, 2000 employees, $500M revenue. US-Central co-lo, Java/Tomcat monolith, Oracle RAC 20TB, batch inventory sync.
Business Req: Handle 10x Black Friday spikes, personalized recommendations, modernize to microservices.
Execs: CEO wants omnichannel; CFO needs predictable spend; CTO demands zero downtime cutover.
Tech Req: PCI-DSS compliance, automated image processing, real-time inventory, CI/CD.
Constraints: Complex Oracle stored procedures, team learning containers, strict bi-annual audits.
QUESTION:
How should you design the automated image processing pipeline for the product catalog?
Answer options:
Upload images to a Compute Engine VM and run a cron job every hour to process them.
Upload images to Cloud Storage, trigger a Cloud Function via Eventarc, and process the image.
Store images in Cloud SQL as BLOBs and use database triggers to process them.
Stream images through Cloud Pub/Sub directly to BigQuery.
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