Google PCA Design and plan a cloud solution architecture Practice Question
A retail company runs its order-processing platform on Compute Engine instances in a single managed instance group (MIG) spread across three zones in us-central1. During seasonal peaks, the application must handle up to 10x normal traffic while keeping median request latency under 200 ms. The architecture team wants to add a caching layer that can absorb repeated catalogue reads and survive the loss of an entire zone without manual intervention. Which design should they choose?
⚠ Common exam trap
The trap here is assuming that any in-memory cache is equally resilient, when the Basic Tier is single-node and cannot survive the loss of a zone.
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
✓
Deploy Memorystore for Redis in Standard Tier with a replica in a second zone and configure the application to read from the Redis endpoint.
The requirement combines zone-level resilience, low-latency repeated reads, and no manual failover. A managed in-memory cache with automatic cross-zone replication satisfies all three simultaneously: replication handles the zone loss, in-memory storage handles the latency, and the managed endpoint removes manual steps. Options that lack a replica or that push orchestration onto the team fail at least one stated constraint, and edge HTTP caching does not fit dynamic internal reads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy Memorystore for Redis in Standard Tier with a replica in a second zone and configure the application to read from the Redis endpoint.
Why this is correct
Memorystore for Redis Standard Tier provides automatic replication to a replica in a different zone and failover if the primary zone is lost, which satisfies the zone-survival requirement. Redis itself absorbs repeated catalogue reads at sub-millisecond latency, offloading the MIG instances. Because the service endpoint is stable, the application needs no manual reconfiguration when failover occurs, matching the no-manual-intervention constraint.
- ✗
Deploy Memorystore for Redis in Basic Tier and place the instance in the same zone as the majority of the MIG instances to reduce network latency.
Why it's wrong here
Basic Tier runs a single node with no replica, so a zone outage destroys the cache and any data it holds, directly violating the survive-a-zone-loss requirement. Co-locating the cache with the busiest zone also concentrates risk. Basic Tier is cheaper and fine for pure caches where data can be regenerated, but this scenario explicitly demands resilience to the loss of an entire zone without manual intervention.
- ✗
Deploy a self-managed Redis cluster on Compute Engine instances distributed across three zones and manage replication and failover with your own scripts.
Why it's wrong here
A self-managed cluster can technically span zones, but failover orchestration, split-brain handling, and client reconnection logic all become the team's responsibility, contradicting the no-manual-intervention requirement. It also adds operational overhead that a managed service removes. The scenario asks for a design decision, not a build-your-own project, so this option introduces avoidable risk and cost without a stated need to control the Redis internals.
- ✗
Enable Cloud CDN with a backend service pointed at the MIG, and rely on edge caching to serve repeated catalogue reads.
Why it's wrong here
Cloud CDN caches HTTP responses at Google's edge, which helps for cacheable static or public content, but the order-processing catalogue reads are dynamic application data typically fetched over internal protocols, not cacheable HTTP GETs. CDN also does not provide the in-memory key-value semantics the application expects. It addresses latency at the edge rather than reducing load on the application tier as required.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
Key term
Compute Engine
Compute Engine is Google Cloud's Infrastructure-as-a-Service (IaaS) offering that lets you create and run virtual machines on Google's infrastructure.
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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 PCA 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 PCA exam.