CKAD Application Observability and Maintenance Practice Question
This CKAD practice question tests your understanding of application observability and maintenance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your team manages a microservices application on a Kubernetes cluster. A critical service 'order-service' is deployed with 3 replicas. Lately, customers have reported occasional timeouts when placing orders. You suspect that the service is overloaded during peak hours. You have configured a HorizontalPodAutoscaler (HPA) based on CPU utilization, but the autoscaler does not appear to be scaling up quickly enough. Upon inspection, you notice that the HPA is configured with a target CPU utilization of 80%, and the current CPU usage of the pods is around 70%. However, the pods' memory usage is high and growing. The application is also logging slow database queries. Which action is most likely to improve the responsiveness of the service during peak load?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the HPA to also scale based on memory utilization, setting a target average value for memory.
Option A is correct because the HPA is configured only for CPU, but the symptom (high memory usage, slow DB queries) indicates memory pressure is the bottleneck. Adding a memory-based metric to the HPA allows it to scale when memory exceeds the target, addressing the root cause of overload during peak hours. The current CPU at 70% is below the 80% threshold, so the HPA won't trigger on CPU alone, but memory scaling can react to the actual resource constraint.
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.
✓
Modify the HPA to also scale based on memory utilization, setting a target average value for memory.
Why this is correct
Memory-based scaling can detect the actual resource pressure.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of replicas to 2 to force the HPA to react more aggressively.
Why it's wrong here
Reducing replicas increases load on remaining pods, but HPA still uses CPU target.
✗
Increase the CPU target utilization to 90% so the HPA triggers earlier.
Why it's wrong here
A higher target would make scaling even less likely.
✗
Optimize the database queries to reduce response time.
Why it's wrong here
While beneficial, it does not directly address the scaling mechanism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume CPU is the only metric for HPA scaling, but the CKAD exam tests the ability to identify when other metrics (like memory) are more relevant based on application behavior and symptoms.
Detailed technical explanation
How to think about this question
The HPA in Kubernetes can scale on multiple metrics, including custom and external metrics, using the `metrics` field in the HPA spec. When memory utilization is high but CPU is low, the HPA will only scale if memory is included as a metric; otherwise, it remains idle. Under the hood, the HPA calculates the desired replica count as `ceil(currentReplicas * (currentMetricValue / targetMetricValue))`, so adding a memory target with a threshold (e.g., 80%) ensures scaling when memory pressure rises, even if CPU is below its target.
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
A practitioner preparing for the CKAD exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this CKAD question in full detail.
Application Observability and Maintenance — This question tests Application Observability and Maintenance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Modify the HPA to also scale based on memory utilization, setting a target average value for memory. — Option A is correct because the HPA is configured only for CPU, but the symptom (high memory usage, slow DB queries) indicates memory pressure is the bottleneck. Adding a memory-based metric to the HPA allows it to scale when memory exceeds the target, addressing the root cause of overload during peak hours. The current CPU at 70% is below the 80% threshold, so the HPA won't trigger on CPU alone, but memory scaling can react to the actual resource constraint.
What should I do if I get this CKAD question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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