- A
Node CPU usage percentage
High CPU may indicate need to scale.
- B
API server request latency
Why wrong: Important for control plane, not directly for workload optimization.
- C
Network bytes sent and received
Why wrong: Less critical for cluster optimization.
- D
Disk I/O throughput
Disk performance affects application speed.
- E
Pod memory usage percentage
Memory pressure can cause OOM kills.
Question 491 of 997
Key Metrics to Monitor for AKS Performance
This AZ-204 practice question tests your understanding of monitor, troubleshoot, and optimize azure solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE metrics should you monitor to determine if an Azure Kubernetes Service (AKS) cluster is running optimally? (Choose three.)
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
✓
Node CPU usage percentage
Node CPU usage percentage (A) is a critical metric because sustained high CPU usage on nodes indicates resource contention, which can degrade pod performance and trigger the kubelet to evict pods. Monitoring this helps ensure the cluster has sufficient compute capacity and that horizontal pod autoscalers or node autoscalers are configured correctly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Node CPU usage percentage
Why this is correct
High CPU may indicate need to scale.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
API server request latency
Why it's wrong here
Important for control plane, not directly for workload optimization.
- ✗
Network bytes sent and received
Why it's wrong here
Less critical for cluster optimization.
- ✓
Disk I/O throughput
Why this is correct
Disk performance affects application speed.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Pod memory usage percentage
Why this is correct
Memory pressure can cause OOM kills.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse control-plane metrics (like API server latency) with worker-node metrics, or they select network throughput as a generic performance indicator without realizing that it does not directly reflect cluster optimization or resource health.
Detailed technical explanation
How to think about this question
Pod memory usage percentage (E) is essential because memory pressure can cause OOMKilled pods and node memory pressure, leading to pod evictions. Disk I/O throughput (D) matters for stateful workloads; high disk latency or throughput saturation can throttle application performance, especially for databases or logging agents. Under the hood, the kubelet uses cAdvisor to collect these metrics, and tools like Prometheus with the kube-state-metrics exporter aggregate them for alerting.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Monitor, troubleshoot, and optimize Azure solutions — This question tests Monitor, troubleshoot, and optimize Azure solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Node CPU usage percentage — Node CPU usage percentage (A) is a critical metric because sustained high CPU usage on nodes indicates resource contention, which can degrade pod performance and trigger the kubelet to evict pods. Monitoring this helps ensure the cluster has sufficient compute capacity and that horizontal pod autoscalers or node autoscalers are configured correctly.
What should I do if I get this AZ-204 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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