Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution
You are investigating high latency in your application deployed on Compute Engine. You suspect a specific API call is taking longer than expected. Which Google Cloud tool should you use to analyze the latency of individual requests?
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
✓
Cloud Trace
Cloud Trace provides distributed tracing, allowing you to see the latency of individual requests and identify bottlenecks. It captures trace spans from supported frameworks and services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Debugger
Why it's wrong here
Cloud Debugger lets you inspect the variable values and call stack at a specific line of code in a running application without stopping it. It is meant for diagnosing logic errors or state bugs, not for measuring how long a request spends in different services. Because it captures a snapshot at a single point, it cannot show the end-to-end duration or the per-span timings needed to locate a latency bottleneck.
- ✓
Cloud Trace
Why this is correct
Cloud Trace is a distributed tracing service designed to collect latency data from Google Cloud and measure time spent in each service and API call during a request. It provides detailed per-request traces with spans that show the timing of each operation, making it the correct tool to investigate high application latency. By analyzing the waterfall view of spans, you can identify the exact component responsible for the delay across distributed services.
- ✗
Cloud Monitoring dashboards
Why it's wrong here
Cloud Monitoring dashboards aggregate metrics such as CPU utilization, memory usage, request count, and error rates into charts and alerts. While they can show average latency over time, they do not provide the per-request trace-level breakdown needed to isolate where within a request that latency occurs. Dashboards lack distributed tracing capabilities and cannot correlate spans across services for a single user request, so they are insufficient for root-causing latency issues.
- ✗
Cloud Logging log explorer
Why it's wrong here
Cloud Logging log explorer stores structured and unstructured log entries with timestamps, but it is not designed to analyze request latency. You could manually calculate timestamps from log entries, yet that would lack the precise span-level breakdown and critical path information that trace data offers. Even with trace IDs embedded in logs, the log explorer requires external correlation and does not natively render latency waterfall diagrams, making it a poor fit for this investigation.
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Related to this question
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Google Cloud Platform Overview
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
Key term
Trace
A trace is a record of the path and timing of a request or operation as it moves through components in a system, used to monitor performance and troubleshoot issues.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.