KCNA Cloud Native Architecture Practice Question
A company is evaluating cloud-native observability practices for its Kubernetes-based microservices. Which TWO practices are essential for achieving effective observability? (Choose two.)
⚠ Common exam trap
The trap here is equating observability with simple log checking or assuming that reducing overhead by disabling health checks is beneficial, when in fact observability requires automated collection of metrics and centralized logs.
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
✓
Aggregating logs into a centralized system
Effective observability in cloud-native systems relies on the three pillars: metrics, logs, and traces. Collecting metrics and aggregating logs are foundational because they provide quantitative and qualitative insights into distributed applications. Together they enable monitoring, alerting, and debugging across ephemeral and dynamic environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Aggregating logs into a centralized system
Why this is correct
Logs capture discrete events and errors that are crucial for debugging and root cause analysis. Centralizing logs from distributed services allows correlation across components. In dynamic environments where pods are ephemeral, centralized log aggregation is essential to retain and query logs, making it a fundamental observability practice.
- ✓
Collecting metrics from all services and infrastructure
Why this is correct
Metrics provide quantitative data on performance and resource usage, enabling monitoring and alerting. In cloud-native systems, metrics from services, pods, and nodes are essential for understanding system health and detecting anomalies. Without metrics, teams lack visibility into trends and cannot proactively address issues, making this a core observability practice.
- ✗
Storing all data in a single monolithic database
Why it's wrong here
A monolithic database does not contribute to observability; it may even hinder it by creating a single point of failure and limiting scalability. Observability focuses on collecting and analyzing telemetry data from distributed components. This choice is unrelated to observability best practices and is not essential.
- ✗
Disabling health checks to reduce overhead
Why it's wrong here
Health checks are vital for maintaining service availability and enabling automated recovery. Disabling them would prevent Kubernetes from detecting and replacing unhealthy pods, reducing reliability. This practice harms observability because it removes signals about service health, and it is not an observability practice.
- ✗
Using only manual inspection of individual pod logs
Why it's wrong here
Manual inspection does not scale in microservices environments with many ephemeral pods. It is error-prone and cannot provide a holistic view. Observability requires automated collection and correlation, not manual per-pod checks. This practice fails to meet the needs of distributed systems and is not considered essential.
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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 CNCF exam blueprint
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