Scheduling, Containerization and Workload Basics
This topic connects container images, Deployments, ReplicaSets, Jobs, DaemonSets, requests, limits, scheduling constraints, and self-healing behavior.
How to study for Kubernetes and Cloud Native Associate
Treat each question as a foundation check: identify the Kubernetes primitive, connect it to the cloud native pattern, then choose the least surprising operational behavior.
Core concepts
Concept 1
The scheduler filters infeasible Nodes, scores feasible candidates, and binds each whole Pod to one Node.
Exam cue: Decide whether the workload is continuous, stateful, per-Node, one-time, or scheduled run-to-completion work.
Concept 2
Requests express scheduling demand, limits constrain runtime use, and affinity, taints, topology, and priority refine placement.
Exam cue: For a Pending Pod, compare requests with per-Node allocatable capacity and then inspect hard placement constraints.
Concept 3
Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs encode different workload lifecycle intentions.
Exam cue: Remember that toleration permits a taint; affinity or selection attracts a Pod.
Risk pitfalls and guardrails
Adding free capacity across Nodes as though one Pod could span them.
Guardrail: Avoid answers that rely only on habit, ignore the stated source, skip safety or compliance steps, or choose convenience over the professional standard.
Using a Deployment for a process that should complete once and stop.
Guardrail: Avoid answers that rely only on habit, ignore the stated source, skip safety or compliance steps, or choose convenience over the professional standard.
Assuming high priority can overcome missing hardware, topology, image compatibility, or hard policy.
Guardrail: Avoid answers that rely only on habit, ignore the stated source, skip safety or compliance steps, or choose convenience over the professional standard.
Memory anchors
Container Image
A container image packages application filesystem layers, metadata, entrypoint, and dependencies.
Deployment
A Deployment manages replicated stateless Pods and supports rollout and rollback behavior.
ReplicaSet
A ReplicaSet maintains a desired number of matching Pods and is often managed by a Deployment.
Job
A Job runs Pods to completion and tracks successful completions.
DaemonSet
A DaemonSet runs a Pod on selected nodes, commonly for node-level agents.
Request
A resource request reserves scheduling capacity and influences Pod placement.
Limit
A resource limit caps container usage and may trigger throttling or termination.
Self-Healing
Self-healing means controllers recreate or reschedule workloads when actual state drifts from desired state.
Taint and Toleration
A taint repels Pods; a matching toleration removes that rejection but does not guarantee placement.
StatefulSet
A StatefulSet provides stable ordinal identity and storage relationships for stateful replicas.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Which two broad stages describe the default scheduler's choice of a node for a pending Pod?
A Pod requests 2 CPUs, but each node has only 1 CPU of unrequested allocatable capacity. What should the scheduler do?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
What is Pass Harbor?
Completely free exam prep for 317 U.S. exams.
- Practice questions
- Flashcards
- Study guides
- Mock exams
- No registration
- No paywall
- Start instantly
“No more expensive exam prep. Quality study tools should be accessible to everyone.”