Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution

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 Monitoring

  Why this is correct

  Cloud Monitoring is the correct starting point because it provides time-series metrics for Compute Engine, such as CPU utilization, memory usage, disk I/O, and network throughput. You can build custom dashboards and alerts to correlate intermittent latency spikes with resource saturation, helping you determine whether the cause is a bottleneck in the VM, disk, or network. This metric-centric view is essential for seeing the pattern of when slowdowns occur.

• ✓

  Cloud Logging

  Why this is correct

  Cloud Logging is correct because it aggregates application stdout/stderr, system logs, and access logs, allowing you to search for error messages, stack traces, or slow-query entries that align with the latency events. Using Logs Explorer with timestamp filters and log-based metrics, you can correlate specific exceptions or warnings with the intermittent behavior. This helps uncover application-level faults, such as database connection pool exhaustion or failed dependencies.

• ✗

  Cloud Profiler

  Why it's wrong here

  Cloud Profiler is not suitable because it performs continuous, low-overhead sampling of CPU and heap usage, producing aggregated profiles that show where functions consume resources over the long term. It lacks the temporal granularity needed to catch short-lived, intermittent latency spikes, as it does not tie samples to individual request latency or specific time windows. Profiler is better for optimizing sustained performance rather than troubleshooting transient slowdowns.

• ✗

  Cloud Debugger

  Why it's wrong here

  Cloud Debugger is not suitable because it captures code snapshots and log points in production, allowing you to inspect variable values and program state at a specific source line without stopping the VM. It does not measure request latency, trace execution paths, or analyze network or I/O bottlenecks, so it cannot reveal why a particular request is slow. Debugger is meant for diagnosing code bugs and logical errors, not performance or latency issues.

• ✓

  Cloud Trace

  Why this is correct

  Cloud Trace is correct because it provides distributed tracing that captures latency for individual requests, breaking down the time spent in each service, library, and remote call. By analyzing trace spans and latency distributions, you can pinpoint the exact component—such as a Compute Engine instance, Cloud SQL query, or external API—that contributes the most delay. Trace's ability to sample and compare traces across requests makes it ideal for isolating intermittent bottlenecks.

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