How to Speed Up Ecommerce Page Loads on Google Cloud
Short answer
BC Serve images and videos through a CDN on the external HTTPS load balancer and cache product data in Memorystore for Redis.
Question
Your ecommerce web application includes many products with pictures and videos. You need to improve the application's page load speed and reduce the latency for customer requests. What should you do? (Choose two.)
Key Takeaways
The question tests caching strategy for media-heavy pages, and the trap is picking compute-platform changes (Cloud Run concurrency or GKE Autopilot) instead of the two layers that actually put data closer to the customer.
For an ecommerce site serving images and videos, page load speed and request latency are best improved by caching static media at the edge with a CDN on the external HTTPS load balancer (C) and caching dynamic data in Memorystore for Redis (B). This page explains why those two options are the correct pair and why platform-migration choices do not address latency.
Common mistake: Option A (Cloud Run with concurrency) is the most tempting wrong pick: scaling request handling raises throughput but does not move images, videos, or cached data nearer to end users, so page load speed and perceived latency stay the same.
Expert Analysis
Why the Answer Is Correct
A CDN attached to the application's external HTTPS load balancer caches the product pictures and videos at Google edge points of presence, so customers download large static objects from a location near them instead of from the origin, directly cutting page load time. Memorystore for Redis stores frequently read, low-latency data such as product details, prices, categories, and session state in memory, which removes repeated round trips to the backend database for every page view. Together the two options attack both halves of the problem: static assets served from the edge and dynamic lookups served from an in-memory store. This is the standard Google Cloud pattern for latency-sensitive, media-heavy web applications.Why the Other Options Are Wrong
Option A, enabling concurrency on Cloud Run, lets a single instance handle more simultaneous requests, which improves throughput under load but does not shorten the distance or time needed to fetch images, videos, or database rows. Option D, migrating from Bigtable to Spanner, changes the storage engine and its consistency model rather than reducing the number of hops a customer request makes, so it does not address the stated page-load requirement. Option E, moving from standard GKE to GKE Autopilot, is a cluster-management and node-provisioning difference with no meaningful effect on content delivery latency for static media. None of these place cached content closer to the user, which is what the question is really asking for.Community Comment Notes
The single community comment supports the B and C pairing by separating the two cache roles: Redis holds product details, prices, categories, and session data to reduce database calls, while the CDN serves the pictures and videos close to users. The commenter also pointed out that "Pictures and videos are static/media content" and therefore belong at the edge rather than in an application cache. No comment argued for Cloud Run, Spanner, or GKE Autopilot as a latency fix, and no dissenting votes are recorded.Official Reference
Exam Strategy
When a question names specific media types (pictures and videos) plus latency, first look for an edge-caching option such as a CDN on the load balancer, then pair it with an in-memory cache like Memorystore for Redis; treat compute-platform swaps as distractors unless scaling is the stated goal.
Frequently Asked Questions
Why is enabling Cloud Run concurrency not a valid latency fix?
Concurrency increases how many requests one instance handles at once, improving throughput under load, but it does not move images, videos, or data closer to the customer, so page load speed is unchanged.
Why not switch from Bigtable to Spanner to reduce latency?
Changing databases does not reduce the number of network hops a customer request makes; caching at the edge and in memory is what actually cuts the time to first byte for media-heavy pages.
Discussion 1 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded.
From public community threads 1
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0d8df8b 👍 1Redis can cache frequently used data like: Product details Prices Categories Session data This reduces database calls and improves response speed. Pictures and videos are static/media content. CDN caches them close to users, so pages load faster.
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