The answer is implementing request hedging by sending duplicate requests to multiple replicas, along with using a load balancer that employs a least-request algorithm. These two actions directly reduce tail latency in microservices on GKE by preventing any single overloaded pod from becoming a bottleneck; hedging ensures that if one replica is slow, a duplicate request to another replica can return faster, while the least-request algorithm minimizes queuing delay by routing traffic to the pod with the fewest active connections. On the Google Professional Cloud DevOps Engineer exam, this concept tests your understanding of how to mitigate the “long tail” of response times in distributed systems—a common trap is to focus only on scaling replicas without considering request distribution or redundancy. A useful memory tip is to think of “hedge your bets and balance the load” to recall that both redundancy and intelligent routing are required to tame tail latency.
PCDOE Optimizing service performance Practice Question
This PCDOE practice question tests your understanding of optimizing service performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO actions can reduce tail latency in a microservices architecture deployed on GKE? (Choose 2)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Run multiple replicas of each service and use a load balancer with least-request algorithm.
Option A is correct because running multiple replicas and using a load balancer with a least-request algorithm distributes incoming requests to the pod with the fewest active connections, reducing queuing delay and preventing any single replica from becoming a hotspot. This directly lowers tail latency by ensuring that slow or overloaded pods are not overwhelmed, and the load balancer's algorithm minimizes the variance in response times across replicas.
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.
✓
Run multiple replicas of each service and use a load balancer with least-request algorithm.
Why this is correct
Distributes load and reduces queuing delay.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a single large machine type for all services.
Why it's wrong here
Not a targeted solution for tail latency.
✗
Enable session affinity to keep users on the same pod.
Why it's wrong here
Session affinity can cause uneven load and increase tail latency.
✗
Increase the batch size for processing requests.
Why it's wrong here
Larger batches increase latency for each request.
✓
Implement request hedging by sending duplicate requests to multiple replicas.
Why this is correct
Hedging reduces tail latency by using the fastest response.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that session affinity (sticky sessions) improves performance, but in reality it harms tail latency by preventing even load distribution and causing pod overload under variable traffic.
Detailed technical explanation
How to think about this question
Request hedging (Option E) works by sending the same request to multiple replicas and using the first successful response, which reduces tail latency by masking slow pods; this is effective when the service is idempotent and the overhead of duplicate work is acceptable. Under the hood, GKE's load balancer with least-request algorithm uses the Envoy proxy's 'least_request' load balancing policy, which tracks active requests per upstream endpoint and selects the one with the fewest, reducing the impact of long-tailed request distributions.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Run multiple replicas of each service and use a load balancer with least-request algorithm. — Option A is correct because running multiple replicas and using a load balancer with a least-request algorithm distributes incoming requests to the pod with the fewest active connections, reducing queuing delay and preventing any single replica from becoming a hotspot. This directly lowers tail latency by ensuring that slow or overloaded pods are not overwhelmed, and the load balancer's algorithm minimizes the variance in response times across replicas.
What should I do if I get this PCDOE 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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Question Discussion
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