CKAD Application Design and Build Practice Question
A pod is stuck in 'Pending' state. You run 'kubectl describe pod mypod' and see the event: '0/4 nodes are available: 4 Insufficient memory'. Which action will resolve the issue?
⚠ Common exam trap
It's easy for candidates to think increasing resource requests or adding selectors will help, but those actions either increase demand or restrict placement, making the problem worse instead of solving the underlying resource shortage.
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
✓
Scale down other pods in the cluster to free memory
The pod is stuck in 'Pending' because no node has enough allocatable memory to satisfy its memory request. Scaling down other pods frees memory on existing nodes, allowing the scheduler to place the pod. This directly resolves the 'Insufficient memory' condition without changing the pod's specification or cluster topology.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Scale down other pods in the cluster to free memory
Why this is correct
When a pod is Pending, one common cause is that no node has enough allocatable memory because the sum of memory requests from pods already assigned to each node is at or near the node's capacity. Scaling down other pods (for example, reducing Deployment replicas) lowers the total requested memory on those nodes, freeing enough headroom for the scheduler to place the pending pod. This directly addresses the scheduling failure, though it may require coordination with other workloads.
- ✗
Add a nodeSelector to target a specific node
Why it's wrong here
A nodeSelector constrains scheduling to a subset of nodes by label, but it does nothing to change the amount of available memory on any node. If every node already has insufficient allocatable memory, the scheduler will still report '0/3 nodes are available: insufficient memory' after filtering by the selector. Targeting a specific node can even make the situation worse by removing other nodes from the eligible set, so this option does not solve the underlying resource shortage.
- ✗
Increase the memory request in the pod spec
Why it's wrong here
Increasing the memory request in the pod spec makes the pod more demanding: the scheduler requires that a node have at least that amount of unallocated, allocatable memory. Since the pod is already unschedulable because no node can satisfy its current request, raising the request only increases the minimum capacity needed and further reduces the set of candidate nodes. This makes the Pending condition more likely, not less, and should never be used to resolve a memory-constrained scheduling failure.
- ✗
Scale up the Deployment to create more pod replicas
Why it's wrong here
Scaling up the Deployment requests more replicas, and every new replica carries the same memory request as the original pod. This adds more resource claims to the cluster, increasing the total requested memory and making the existing insufficient-memory problem more severe. Even if the original pod remains in the Deployment, scaling up does not reduce memory consumption on nodes; instead, it consumes additional scheduling slots and may push the cluster further into an unschedulable state.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CKAD practice question is part of Courseiva's free CNCF certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CKAD exam.