Cloud Digital Leader Why Cloud Technology Can Transform Business Practice Question
A company wants to modernize its legacy monolithic application by adopting microservices on Google Cloud. They need to ensure high availability and manage service-to-service communication. Which THREE Google Cloud services should they use? (Choose THREE.)
⚠ Common exam trap
GCDL often tests the distinction between core microservices infrastructure (GKE, Service Mesh, Load Balancing) and supporting services (Cloud Storage, Cloud Functions), causing candidates to select serverless or storage options as if they were orchestration components.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cloud Service Mesh (or Istio)
Cloud Service Mesh (or Istio) (A) is correct because it provides the service-to-service communication layer for microservices, delivering traffic management, mutual TLS, observability, and policy enforcement across services. Google Kubernetes Engine (GKE) (B) is correct because it is the managed Kubernetes platform used to deploy, orchestrate, and scale the containerized microservices with high availability across zones and regions. Cloud Load Balancing (D) is correct because it distributes traffic to the microservices and provides global, highly available entry points with health checks and failover. Cloud Storage (C) is not a compute or service-mesh platform for running microservices; it is object storage. Cloud Functions (E) is a serverless functions platform suited to event-driven, single-purpose workloads rather than hosting and managing a full microservices architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud Service Mesh (or Istio)
Why this is correct
Cloud Service Mesh (Istio) injects Envoy sidecar proxies into each pod, creating a data plane that intercepts and controls east-west traffic between microservices. It provides mutual TLS, traffic splitting, retries, and rich telemetry, while the control plane manages policies via custom resource definitions. Because it operates transparently at the application layer, teams can enforce security and reliability without modifying service code.
- ✓
Google Kubernetes Engine (GKE)
Why this is correct
Google Kubernetes Engine (GKE) serves as the managed Kubernetes control plane and worker pool that schedules, scales, and heals containerized microservices. It automates rolling deployments, self-heals failed pods, and exposes services via ClusterIP, NodePort, and LoadBalancer types, making it the runtime foundation for a microservices platform. However, GKE alone does not offer the L7 policy and observability features that a service mesh layer provides.
- ✗
Cloud Storage
Why it's wrong here
Cloud Storage is a fully managed object store for immutable binary data such as media files, database backups, and data lake archives, with no compute or traffic-routing capabilities. It cannot execute containers, host long-running processes, or mediate requests between services. While microservices may use it to persist static assets or transaction logs, it is not a communication or orchestration plane and therefore cannot modernize the application's runtime.
- ✓
Cloud Load Balancing
Why this is correct
Cloud Load Balancing is a distributed, anycast-based L4/L7 service that routes incoming user traffic to healthy backend microservice instances across regions, enabling high availability and automatic scaling. It terminates SSL, applies URL-based routing, and integrates with GKE ingress controllers, but it only manages north-south (external to service) traffic, not intra-cluster service-to-service calls.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions is a serverless FaaS offering that runs short-lived, event-triggered code (e.g., HTTP endpoints or Pub/Sub handlers) with a default timeout of 60 seconds and a maximum of 540 seconds, designed for stateless, burstable workloads. It lacks support for persistent connections, service discovery, and sidecar injection, so it cannot sustain the long-running, stateful interactions of a monolithic microservice decomposition. Its per-invocation concurrency and scaling model also make it unsuitable as a general-purpose microservice host.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Lifecycle Management on Google Cloud
Key term
Cloud Functions
Cloud Functions are serverless compute services that let you run single-purpose code in response to events without managing servers.
Key term
Availability
Availability is the measure of how often a system or service is operational and accessible when needed, typically expressed as a percentage of uptime.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.