12.2 Comprehensive Case Studies: EHR Healthcare & Cymbal Retail
Key Takeaways
- EHR Healthcare is a SaaS electronic health record provider replacing colocation facilities with Google Cloud; its stated business requirements include a minimum 99.9% availability for customer-facing systems and rapid onboarding of new insurance providers.
- EHR Healthcare's legacy file- and API-based insurance integrations remain on-premises for years, so the correct architecture maintains hybrid connectivity (Dedicated/Partner Cloud Interconnect or HA VPN) instead of forcing a big-bang migration.
- Because EHR Healthcare requires only 99.9% availability, regional high-availability managed services (regional GKE, Cloud SQL HA) satisfy the requirement at far lower cost than multi-region Cloud Spanner — matching the solution to the stated SLO is the exam skill.
- Cymbal Retail's transformation has three tracks: generative-AI catalog and content enrichment, conversational commerce with product discovery, and technical stack modernization with explicit cost goals of reducing call-center staffing and data-center hosting spend.
- Cymbal Retail maps to Vertex AI generative models (Imagen class) for image variation, Vision AI / Natural Language AI / Document AI for attribute extraction, Vertex AI Search for product discovery, and Conversational Agents to replace its IVR call tree — all gated by a human-in-the-loop associate review UI.
Last updated: August 2026
Comprehensive Case Studies: EHR Healthcare & Cymbal Retail
Architectural Objective: Each Professional Cloud Architect exam presents 2 of the 4 published case studies, and case study questions make up roughly 20-30% of the exam. This section provides complete architectural teardowns for EHR Healthcare and Cymbal Retail, grounded in what the official case studies actually state: their existing environments, business requirements, technical requirements, and executive priorities.
Case Study 1: EHR Healthcare (In-Depth Teardown)
Company Overview & Existing Technical Environment
EHR Healthcare is a leading provider of electronic health record software, delivered as SaaS to multinational medical offices, hospitals, and insurance providers. Rapid industry change has produced exponential year-over-year growth, and the company has chosen Google Cloud to replace its colocation facilities — one data center lease is about to expire, creating time pressure for migration.
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| EHR HEALTHCARE EXISTING ENVIRONMENT |
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| COMPUTE | Web apps; many recently containerized on Kubernetes clusters. |
| DATA STORES | Mixed MySQL, MS SQL Server, Redis, and MongoDB databases. |
| INTEGRATIONS | Legacy file- and API-based insurance provider integrations, |
| | on-premises; scheduled for replacement, but NOT moving now. |
| IDENTITY | Microsoft Active Directory-managed users. |
| MONITORING | Assorted open-source tools; email alerts are frequently ignored. |
+-----------------------------------------------------------------------------------+
Business Requirements (What the Case Study Actually Mandates)
- On-board new insurance providers as quickly as possible — interface ingestion speed is a revenue constraint.
- Minimum 99.9% availability for customer-facing systems — note this is three nines, NOT five nines; the correct exam answer is regional HA managed services, not an exotic multi-region active-active build.
- Centralized visibility and proactive action on system performance and usage — replaces the ignored email alerts.
- Healthcare-trend insights and predictions from provider data — an analytics requirement pointing at a managed warehouse.
- Reduce latency to all customers, maintain regulatory compliance (HIPAA-class healthcare obligations, executed via a Business Associate Agreement with Google Cloud), and decrease infrastructure administration cost — favoring managed services over self-managed VMs.
Technical Requirements
- Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers.
- Provide a consistent way to manage container-based customer-facing applications and manage multiple container environments.
- Provide a secure, high-performance connection between on-premises systems and Google Cloud.
- Provide consistent logging, log retention, monitoring, and alerting capabilities.
- Dynamically scale and provision new environments on demand.
- Create interfaces to ingest and process data from new providers.
EHR Healthcare Target Architecture
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| EHR HEALTHCARE TARGET CLOUD TOPOLOGY |
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| |
| ON-PREMISES COLOs GOOGLE CLOUD |
| +---------------------+ +---------------------------------------+ |
| | Legacy insurer | | Dedicated/Partner Cloud Interconnect | |
| | file/API interfaces | ===== BGP ===> | + HA VPN (backup / low-volume links) | |
| +---------------------+ +-------------------+-------------------+ |
| | |
| v |
| +---------------------------------------+ |
| | Pub/Sub + Dataflow provider ingestion | |
| | - New insurer feeds normalized | |
| | - Streams to BigQuery (trend analytics| |
| | and prediction) | |
| +-------------------+-------------------+ |
| | |
| v |
| +---------------------------------------+ |
| | Regional GKE (patient portal & SaaS) | |
| | - GKE Enterprise/Anthos for on-prem | |
| | container consistency | |
| +---------+-------------------+---------+ |
| | | |
| v v |
| +-----------------------+ +--------------------+ |
| | Cloud SQL (HA) for | | Memorystore | |
| | MySQL / MS SQL Server | | for Redis | |
| +-----------------------+ +--------------------+ |
| OPERATIONS: Cloud Monitoring dashboards + alert policies (replacing email), |
| Cloud Logging unified retention, Cloud Build + Cloud Deploy continuous rollout.|
+-----------------------------------------------------------------------------------+
Core Architectural Decisions for EHR Healthcare
- Hybrid connectivity sized to traffic: Dedicated or Partner Cloud Interconnect for the high-volume SaaS traffic, with HA Cloud VPN as backup and for low-volume or temporary links. Public-internet endpoints for health data fail the regulatory-compliance constraint.
- Container consistency without a big-bang move: Containerized customer-facing apps move to regional GKE clusters; GKE Enterprise (Anthos) provides the consistent management plane across the multiple on-premises and cloud container environments the case study lists. Legacy insurer integrations deliberately stay put — the case study says they are not moving — so the architecture must bridge, not migrate, them.
- Availability matched to the stated target: The 99.9% requirement is met by regional HA configurations (multi-zone GKE, Cloud SQL High Availability with automatic zonal failover). Recommending multi-region Cloud Spanner at 99.999% overspends against a requirement the business never made — a classic distractor.
- Operations overhaul: Consolidated Cloud Monitoring dashboards and alerting policies (paging, not email), unified Cloud Logging retention, and Cloud Build + Cloud Deploy pipelines to deliver the requested continuous deployment cadence. Database Migration Service handles the MySQL/SQL Server-to-Cloud SQL moves with minimal downtime.
Case Study 2: Cymbal Retail (In-Depth Teardown)
Company Overview & Solution Concept
Cymbal Retail is a fast-growing online retailer with a large assortment of products across several retail sub-verticals — managing its extensive product catalog is a constant challenge. Cymbal's transformation has exactly three core tracks:
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| CYMBAL RETAIL TRANSFORMATION TRACKS |
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| 1. CATALOG & CONTENT ENRICHMENT | Gen AI generates product attributes, rich |
| | descriptions, and images from supplier data. |
| 2. CONVERSATIONAL COMMERCE | AI virtual agents on website and mobile app |
| & PRODUCT DISCOVERY | converse naturally and surface relevant items. |
| 3. TECHNICAL STACK MODERNIZATION| Cloud infrastructure, secure data handling, |
| | 3rd-party integrations, proactive monitoring. |
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Existing Technical Environment
- Mix of on-premises and cloud-based systems.
- Databases: MySQL, Microsoft SQL Server, Redis, and MongoDB hold the catalog and customer data.
- Kubernetes clusters run containerized applications.
- Legacy SFTP file transfers and ETL batch processing integrate with on-premises systems.
- A custom web app lets customers browse the catalog by querying the relational databases.
- An IVR (Interactive Voice Response) phone tree routes calls; human call-center agents manually key in orders the IVR cannot complete.
- Fragmented open-source monitoring (Grafana, Nagios, Elastic).
Business Requirements
- Automate product catalog enrichment — reduce manual effort, minimize errors, keep information consistent across sales channels.
- Improve product discoverability — better search relevance and efficient product finding.
- Increase customer engagement — a more interactive, personalized shopping experience.
- Drive sales conversion and revenue growth through an intuitive shopping journey.
- Reduce costs — specifically call-center staffing costs and data-center hosting costs (this is why Conversational Agents and consolidated managed infrastructure appear in correct answers).
Technical Requirements & Mapped Architecture
| Cymbal Technical Requirement | Google Cloud Architecture |
|---|---|
| Accurately derive product attributes from supplier titles, descriptions, and images, aligned to catalog categories | Vision AI + Natural Language AI + Document AI for extraction and classification |
| Generate image variations from a base image (colors, backgrounds, text overlays) | Vertex AI generative image models (Imagen class) |
| Process natural-language customer requests and return highly relevant products | Vertex AI Search (enterprise product discovery) |
| AI virtual agents for conversational shopping and support | Conversational Agents replacing the IVR |
| Associates review/manage generated content (approve, reject, modify) before catalog updates | Human-in-the-loop review UI built on the generated-content store |
| Handle the full catalog and anticipated growth without degrading performance or UX | Autoscaling managed services (GKE, Cloud Run) plus consolidated Cloud Monitoring |
| Handle customer data and virtual-agent interactions securely and in compliance with regulations | Sensitive Data Protection, Cloud Armor, Assured Workloads, Security Command Center |
Cymbal Retail Target Architecture
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| CYMBAL RETAIL TARGET CLOUD TOPOLOGY |
+-----------------------------------------------------------------------------------+
| |
| SHOPPERS & SUPPLIERS GOOGLE CLOUD |
| [ Web & Mobile Shoppers ] =====> [ Conversational Agents (virtual agents) ] |
| | Vertex AI Search (discovery) |
| v |
| [ GKE / Cloud Run commerce services ] |
| | |
| [ Supplier files (SFTP/ETL) ] ===> [ Cloud Storage landing ] |
| v |
| [ Vision AI | Natural Language AI | Document AI ] |
| [ Vertex AI (Imagen) image variations ] |
| v |
| [ Human-in-the-loop associate review UI ] |
| v |
| [ Approved catalog content -> production databases ] |
| |
| ANALYTICS: Datastream/Dataflow replication from MySQL/SQL Server into BigQuery. |
| OPERATIONS: Consolidated Cloud Monitoring replacing Grafana/Nagios/Elastic. |
+-----------------------------------------------------------------------------------+
- Generative catalog pipeline: Supplier data lands in Cloud Storage; Vision AI / Natural Language AI / Document AI extract and classify attributes; Vertex AI generative image models produce variations. Nothing reaches the catalog without the associate review UI — the human-in-the-loop gate is an explicit case study requirement, not an optional nicety.
- Conversational commerce: Conversational Agents absorb the IVR and routine calls (directly attacking the call-center cost requirement), while Vertex AI Search powers natural-language product discovery on web and mobile.
- Stack modernization with cost targeting: Migrate MySQL to Cloud SQL with Database Migration Service, Redis to Memorystore, unify analytics in BigQuery fed by Datastream/Dataflow, and consolidate observability into Cloud Monitoring — each move mapped to the reduce-cost business requirement.
Case Study Comparison Matrix: EHR Healthcare vs. Cymbal Retail
| Architectural Attribute | EHR Healthcare | Cymbal Retail |
|---|---|---|
| Industry & Domain | Healthcare SaaS (EHR software) | Online retail & digital commerce |
| Defining Theme | Hybrid migration out of colos without disrupting insurer integrations | Generative AI for catalog, discovery, and support |
| Stated Availability | Minimum 99.9% (customer-facing) | Scalability with anticipated growth (no numeric SLA stated) |
| Compute Strategy | Regional GKE + GKE Enterprise/Anthos for on-prem consistency | GKE / Cloud Run autoscaling commerce services |
| Data Strategy | Pub/Sub + Dataflow provider feeds into BigQuery; Cloud SQL HA / Memorystore migrations | Vision/NL/Document AI enrichment; Vertex AI (Imagen) images; Datastream CDC into BigQuery |
| Security & Compliance | Regulatory compliance via BAA, private hybrid connectivity, CMEK where custody is mandated | Sensitive Data Protection, Cloud Armor, Assured Workloads, Security Command Center |
| Explicit Cost Goal | Decrease infrastructure administration cost | Reduce call-center staffing and data-center hosting cost |
[!IMPORTANT] Exam Watch: Case study questions punish over-engineering and under-reading. For EHR Healthcare, honor the stated minimum 99.9% availability with regional HA managed services, keep the legacy insurer integrations reachable during the phased migration, and modernize observability. For Cymbal Retail, anchor every answer in the three transformation tracks — generative catalog enrichment (Vision/NL/Document AI + Vertex AI image models), conversational commerce (Conversational Agents + Vertex AI Search), and stack modernization with its explicit cost-reduction targets — and never skip the human-in-the-loop review step for generated content.
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Test Your Knowledge
Refer to the EHR Healthcare case study. The business requires a minimum of 99.9% availability for customer-facing systems while decreasing infrastructure administration cost. Which architecture meets the requirement without over-engineering?
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B
C
D
Test Your Knowledge
Refer to the EHR Healthcare case study. The executive statement complains that outages stem from misconfigured systems, inadequate capacity planning, and inconsistent monitoring, and the technical requirements demand consistent logging, monitoring, and alerting. What should the architect implement?
A
B
C
D
Test Your Knowledge
Refer to the Cymbal Retail case study. Cymbal needs customers to find products by expressing needs in natural language on the website and mobile app, and wants AI virtual agents to deliver a personalized, intuitive shopping experience. Which services should the architect recommend?
A
B
C
D
Test Your Knowledge
Refer to the Cymbal Retail case study. Generated product attributes and images must not update the live catalog until associates approve them, and customer data handled by virtual agents must meet industry regulations. Which combination of controls satisfies both requirements?
A
B
C
D