- A
Set Cloud Run max-concurrency to 1 so each instance handles one request
Why wrong: Setting concurrency to 1 increases the number of instances (more API calls, not fewer) — the opposite effect.
- B
Cache third-party API responses in Cloud Memorystore (Redis) with appropriate TTL
Caching API responses in Redis means many Cloud Run instances read from cache instead of calling the third-party API separately — dramatically reducing outbound API call volume.
- C
Enable Cloud CDN on the Cloud Run service to cache outbound requests
Why wrong: Cloud CDN caches responses to inbound requests to Cloud Run — it doesn't cache Cloud Run's outbound calls to third-party APIs.
- D
Migrate to Cloud Functions with a lower default concurrency limit
Why wrong: Cloud Functions concurrency limits reduce parallelism but don't reduce API call volume for a given traffic rate — caching addresses the root cause.
Quick Answer
The correct choice is to cache third-party API responses in Cloud Memorystore (Redis) with an appropriate TTL. This pattern directly reduces third-party API calls from Cloud Run with caching by storing the response from the external service in a shared, in-memory data store, so that when burst traffic spawns many concurrent Cloud Run instances, each instance reads from Redis instead of hammering the external API. Because Memorystore is a managed service that sits outside your Cloud Run instances but is accessible via a VPC connector, it adds no new infrastructure to manage while eliminating rate-limit errors. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of stateless serverless scaling versus stateful caching—a common trap is choosing Cloud CDN or a Pub/Sub buffer, but those don’t reduce per-instance API call volume. Memory tip: “Redis the rate-limit risk” — think of Redis as a shared memory shield that lets all instances reuse one API call’s result.
Google ACE Planning and configuring a cloud solution Practice Question
This ACE practice question tests your understanding of planning and configuring a cloud solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A Cloud Run service calls external third-party APIs that have rate limits. Under burst traffic, the service spawns many concurrent instances, each making direct API calls, causing rate limit errors. What GCP pattern reduces API call volume without adding infrastructure?
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
Cache third-party API responses in Cloud Memorystore (Redis) with appropriate TTL
Caching third-party API responses in Cloud Memorystore (Redis) with an appropriate TTL reduces the number of outbound API calls by serving cached data to multiple concurrent Cloud Run instances. This directly addresses rate-limit errors without adding new infrastructure, as Memorystore is a managed in-memory cache that integrates seamlessly with Cloud Run via a VPC connector.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set Cloud Run max-concurrency to 1 so each instance handles one request
Why it's wrong here
Setting concurrency to 1 increases the number of instances (more API calls, not fewer) — the opposite effect.
- ✓
Cache third-party API responses in Cloud Memorystore (Redis) with appropriate TTL
Why this is correct
Caching API responses in Redis means many Cloud Run instances read from cache instead of calling the third-party API separately — dramatically reducing outbound API call volume.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Cloud CDN on the Cloud Run service to cache outbound requests
Why it's wrong here
Cloud CDN caches responses to inbound requests to Cloud Run — it doesn't cache Cloud Run's outbound calls to third-party APIs.
- ✗
Migrate to Cloud Functions with a lower default concurrency limit
Why it's wrong here
Cloud Functions concurrency limits reduce parallelism but don't reduce API call volume for a given traffic rate — caching addresses the root cause.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse caching inbound responses (Cloud CDN) with caching outbound API responses (a pattern using Memorystore or similar), leading them to select Cloud CDN even though it cannot cache server-to-server calls.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Run instances share no local state, so each instance independently calls the third-party API. By using Redis as a distributed cache with a TTL matching the API's rate-limit window (e.g., 60 seconds for a 100-requests-per-minute limit), multiple instances can read the same cached response, reducing aggregate call volume. A subtle behavior: if the TTL is too long, stale data may be served; if too short, the cache provides little benefit—so the TTL must be tuned to the API's data freshness requirements and rate-limit reset interval.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this ACE question test?
Planning and configuring a cloud solution — This question tests Planning and configuring a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Cache third-party API responses in Cloud Memorystore (Redis) with appropriate TTL — Caching third-party API responses in Cloud Memorystore (Redis) with an appropriate TTL reduces the number of outbound API calls by serving cached data to multiple concurrent Cloud Run instances. This directly addresses rate-limit errors without adding new infrastructure, as Memorystore is a managed in-memory cache that integrates seamlessly with Cloud Run via a VPC connector.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This ACE 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 ACE exam.
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