Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
Your company runs a microservices application on Google Kubernetes Engine (GKE). The development team complains that they lack visibility into which service is causing latency spikes during peak hours. You need to implement a solution that provides distributed tracing and service-level metrics without modifying application code. Which approach should you use?
⚠ Common exam trap
The trap here is assuming that Cloud Trace alone can provide service-level metrics without code changes, when it actually requires instrumentation and does not offer metrics out of the box.
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 Anthos Service Mesh and enable its built-in telemetry features, including Cloud Trace and Cloud Monitoring integration.
Anthos Service Mesh provides automatic telemetry collection, including distributed tracing and service-level metrics, without requiring changes to application code. It leverages sidecar proxies to capture traffic and integrates with Cloud Trace and Cloud Monitoring, giving the needed visibility into latency spikes. Other options either require code instrumentation or do not provide the necessary tracing and metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Install the Cloud Logging agent on each node and create log-based metrics for latency.
Why it's wrong here
The Cloud Logging agent collects logs, but it does not automatically generate distributed traces or service-level latency metrics. Log-based metrics can be created from log entries, but they would not provide the detailed tracing needed to pinpoint latency spikes across microservices. This approach requires application code to log latency data, which is not desired.
- ✗
Use Cloud Profiler to continuously profile the application and identify latency bottlenecks.
Why it's wrong here
Cloud Profiler is designed for analyzing CPU and memory usage of applications, not for distributed tracing or service-level latency metrics. It requires integrating a profiling agent into the application, which typically involves code changes or at least configuration adjustments. It does not provide end-to-end tracing across microservices, so it does not satisfy the requirement.
- ✓
Deploy Anthos Service Mesh and enable its built-in telemetry features, including Cloud Trace and Cloud Monitoring integration.
Why this is correct
Anthos Service Mesh (ASM) provides automatic distributed tracing and service-level metrics without requiring application code changes. It uses sidecar proxies to capture telemetry and integrates with Cloud Trace and Cloud Monitoring. This meets the requirement for visibility into service latency without modifying code, making it the correct solution.
- ✗
Enable Cloud Trace on the GKE cluster and instrument each service with the OpenTelemetry SDK.
Why it's wrong here
Cloud Trace provides distributed tracing, but it requires instrumenting each service with the OpenTelemetry SDK or another tracing library. The requirement is to avoid modifying application code, so this approach does not meet the constraint. Additionally, Cloud Trace alone does not provide service-level metrics; you would need to combine it with other tools.
Go deeper
Related to this question
Learn chapter
Virtual Machine Instances in Compute Engine
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
Key term
Microservices
Microservices is an architectural style where a software application is built as a collection of small, independent services, each handling a specific business function and communicating over a network.
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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.