Hard1 markMultiple Choice
Subtask 1.4: Migration PlanMigration StrategyGKECompute

GCP PCA · Question 01 · Migration Plan

CASE STUDY: TechStream Gaming

Company Overview: TechStream Gaming is a global gaming company with 500 employees and $50M annual revenue.
Current Environment: On-premises data centers in US and EU. 200 servers. MySQL databases (5 TB). Peak users: 2M. Cost: $100K/mo.
Business Requirements: Reduce costs by 40%. Support 5x user growth. Launch in APAC, SA, Africa. Improve deployment to daily.
Executive Statements: CEO: 'Scale rapidly.' CFO: 'Max $100K/mo, ROI 18mo.' CTO: 'Limited cloud exp, 99.95% uptime.'
Technical Requirements: <100ms latency globally. Real-time analytics. 5x traffic spikes. EU data residency. DDoS protection. CI/CD.
Constraints: 12-month migration. Max 4-hour downtime. 20 devs (Java/MySQL), 5 ops (limited cloud). Budget $2M.

QUESTION:
Which migration strategy and compute architecture should you recommend for the game servers to meet the business and technical requirements while addressing the CTO's constraints?

Answer options:

A.

Lift and shift the existing VMs to Compute Engine using Migrate for Compute Engine to ensure the 12-month deadline is met.

B.

Refactor the entire application into serverless Cloud Functions to minimize operational overhead for the ops team.

C.

Replatform the application to Google Kubernetes Engine (GKE) using Autopilot mode.

D.

Deploy the application on Anthos on bare metal in their existing data centers to maintain control while modernizing.

How to approach this question

Analyze the constraints: limited cloud ops experience, need for rapid scaling, daily deployments, and 12-month timeline. Replatforming to managed containers balances modernization with timeline.

Full Answer

C.Replatform the application to Google Kubernetes Engine (GKE) using Autopilot mode.✓ Correct
GKE Autopilot removes the need to manage nodes, addressing the ops team's lack of experience. Containerization enables the CI/CD pipeline for daily deployments and handles the 5x traffic spikes efficiently. A full refactor would miss the 12-month deadline, and lift-and-shift wouldn't meet the scaling or deployment goals.

Common mistakes

Choosing lift-and-shift because of the timeline constraint, ignoring the scaling and deployment requirements.

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