Google PCA Practice Question: Managing and Provisioning a Solution Infrastructure
An organization wants to monitor and alert on custom application metrics from a GKE cluster. They also need to view logs in real-time and create metrics from log content. Which two GCP services should they use? (Choose two.)
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 Monitoring
Cloud Monitoring (B) is correct because it is the GCP service that ingests custom application metrics, lets you build dashboards, and configure alerting policies on those metrics from GKE workloads. Cloud Logging (E) is correct because it collects and streams logs in real time, and its log-based metrics feature lets you create counter or distribution metrics directly from log content. Together they satisfy both requirements: metric monitoring/alerting and real-time log viewing with metrics derived from logs. Error Reporting (A) only aggregates and groups application errors, not general metrics or log-based metrics. Cloud Profiler (C) analyzes CPU and heap usage for performance profiling, not metric alerting or log viewing. Cloud Trace (D) captures distributed latency traces across services, which is unrelated to custom metric alerting or log-based metric creation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Error Reporting
Why it's wrong here
Error Reporting aggregates and groups application exceptions, but it neither ingests custom metrics nor derives metrics from log content. It is tempting because it surfaces application errors, yet the required capabilities belong to Cloud Monitoring (custom metrics and log-based metrics) plus Cloud Logging for real-time log viewing.
- ✓
Cloud Monitoring
Why this is correct
Cloud Monitoring ingests custom application metrics from GKE, evaluates alerting policies against them, and can define log-based metrics from log content. It therefore satisfies both the custom metric alerting and log-derived metric requirements within one service.
- ✗
Cloud Profiler
Why it's wrong here
Cloud Profiler continuously analyses CPU and heap usage of running applications to identify performance hotspots; it neither collects custom metrics nor ingests logs. It is tempting because it is a GKE-adjacent observability tool, and would be correct when the goal is diagnosing code-level resource consumption rather than metric alerting and log-based metrics.
- ✗
Cloud Trace
Why it's wrong here
Cloud Trace records and visualises request latency across distributed services; it does not aggregate custom metrics or provide real-time log viewing and log-based metric creation. It is tempting because it is an observability service for GKE workloads, and would be correct when the requirement is latency analysis across microservice call paths.
- ✓
Cloud Logging
Why this is correct
Cloud Logging ingests, stores and streams application logs in real time, and its log-based metrics feature derives custom metrics from log content. This satisfies both the real-time log viewing and metric-creation requirements in the stem.
Go deeper
Related to this question
Learn chapter
IAM Policies, Service Accounts, and Auditing
Key term
Alerting policy
An alerting policy is a set of rules that defines when to send notifications about a system condition that needs attention.
Key term
Profiler
A profiler is a tool that monitors and analyzes the performance, resource usage, and behavior of software applications or systems to identify bottlenecks and optimize efficiency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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