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Exam Code: CKA

Exam Name Certified Kubernetes Administrator

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CNCF CKA Practice Test 110

Question 1090

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Node worker-c changes to NotReady after an operating system update; determine whether kubelet is running before deleting Pods. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status.
  2. Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service.
  3. Kubernetes network connectivity requires a functioning CNI implementation on nodes; core API objects alone do not create functional Pod networking.
  4. DaemonSets schedule according to node selection and toleration rules; unavailable node agents can break networking or logging on selected workers.

Explanation

Correct Answer

B:

Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. This explains the stated condition. The corresponding action is: On worker-c, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-c from an admin context. The direct acceptance evidence is: Run kubectl get node worker-c and confirm Ready after restoring the actual failed component.

Incorrect Answers

A:

The offered statement is true for this other mechanism: Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status. It does not explain the facts described here, which follow from: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service.

C:

Kubernetes network connectivity requires a functioning CNI implementation on nodes; core API objects alone do not create functional Pod networking. That explanation applies to a different technical failure. Here the controlling principle is: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service.

D:

The offered statement is true for this other mechanism: DaemonSets schedule according to node selection and toleration rules; unavailable node agents can break networking or logging on selected workers. It does not explain the facts described here, which follow from: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service.

Question 1091

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Node worker-c changes to NotReady after an operating system update; determine whether kubelet is running before deleting Pods. Work only within the stated target and verify before declaring success. Which observation best verifies the requested outcome rather than an unrelated state?

  1. Confirm local API server container health and successful kubectl get --raw=/readyz after restoring the valid manifest.
  2. Run kubectl -n fulfillment get daemonset dispatch-api and inspect desired/ready counts after the specific node issue is fixed.
  3. Run kubectl get node worker-c and confirm Ready after restoring the actual failed component.
  4. Verify the node returns to Ready without MemoryPressure and Pod eviction rates stabilize after capacity planning.

Explanation

Correct Answer

C:

Run kubectl get node worker-c and confirm Ready after restoring the actual failed component. This validates the affected object or control path, rather than only showing a command completed. The underlying reason is: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. The intended action is: On worker-c, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-c from an admin context.

Incorrect Answers

A:

This observation would test a different technical dependency: Kubeadm control-plane components are commonly static Pods managed by kubelet; kubectl cannot diagnose them if the API server itself is unavailable. The current issue is governed by: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Its decisive check is “Run kubectl get node worker-c and confirm Ready after restoring the actual failed component”.

B:

The alternative verifies a different condition: DaemonSets schedule according to node selection and toleration rules; unavailable node agents can break networking or logging on selected workers. The current case instead depends on: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Confirm that “Run kubectl get node worker-c and confirm Ready after restoring the actual failed component”.

D:

This observation would test a different technical dependency: Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status. The current issue is governed by: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Its decisive check is “Run kubectl get node worker-c and confirm Ready after restoring the actual failed component”.

Question 1092

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Node worker-c changes to NotReady after an operating system update; determine whether kubelet is running before deleting Pods. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Maintain image garbage-collection, log rotation and filesystem capacity alerts.
  2. Test kubelet/CRI version compatibility and runtime services in node patch runbooks.
  3. Back up control-plane manifests and validate risky edits before reloading static Pods.
  4. Monitor kubelet health and coordinate OS upgrades with supported runtime versions.

Explanation

Correct Answer

D:

Monitor kubelet health and coordinate OS upgrades with supported runtime versions. This reduces recurrence of the specific operational failure. It reinforces the action: On worker-c, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-c from an admin context. The relevant mechanism is: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service.

Incorrect Answers

A:

The suggested review tracks a different technical risk: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Here the sustained control must address: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Choose “Monitor kubelet health and coordinate OS upgrades with supported runtime versions”.

B:

That safeguard is relevant to: Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime. The current failure instead follows from: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Its prevention measure is “Monitor kubelet health and coordinate OS upgrades with supported runtime versions”.

C:

This routine guards against another mechanism: Kubeadm control-plane components are commonly static Pods managed by kubelet; kubectl cannot diagnose them if the API server itself is unavailable. The recurring exposure in the current case is: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. The matching preventive check is “Monitor kubelet health and coordinate OS upgrades with supported runtime versions”.

Question 1093

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: Node worker-a is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which command or initial safe diagnostic step best addresses the stated lab task?

  1. On worker-c, run sudo systemctl status containerd and sudo journalctl -u containerd -n 80 --no-pager before modifying runtime configuration.
  2. Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages.
  3. On worker-a, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-a from an admin context.
  4. Run kubectl -n ledger describe pvc invoice-data and kubectl get storageclass fast-block before recreating either resource.

Explanation

Correct Answer

B:

Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages. This is the appropriate command-line action or first safe diagnostic. The technical reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Validate the result with: Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes. Execute changes only in an authorized disposable lab or after normal production change controls.

Incorrect Answers

A:

The offered action targets: Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime. The case facts point instead to: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Implement “Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages” to address that specific condition.

C:

The proposed action belongs to the mechanism: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. Here the stated fault follows a different mechanism: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The corrective choice is “Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages”.

D:

The offered action targets: A PVC binds only to compatible available storage or successful dynamic provisioning; absent provisioners, quotas or delayed binding can leave it Pending. The case facts point instead to: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Implement “Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages” to address that specific condition.

Question 1094

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: Node worker-a is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. A hard nodeSelector must match a Node's actual labels; tolerations or Service routing cannot bypass the unmatched selector.
  2. The scheduler places Pods by requested resources and constraints, not the current shell's observed free CPU alone.
  3. DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.
  4. Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime.

Explanation

Correct Answer

C:

DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. This explains the stated condition. The corresponding action is: Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages. The direct acceptance evidence is: Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes.

Incorrect Answers

A:

This principle describes a separate cause: A hard nodeSelector must match a Node's actual labels; tolerations or Service routing cannot bypass the unmatched selector. The reported condition is governed by: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The two mechanisms should not be conflated.

B:

The scheduler places Pods by requested resources and constraints, not the current shell's observed free CPU alone. That explanation applies to a different technical failure. Here the controlling principle is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

D:

This principle describes a separate cause: Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime. The reported condition is governed by: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The two mechanisms should not be conflated.

Question 1095

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: Node worker-a is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which observation best verifies the requested outcome rather than an unrelated state?

  1. Run kubectl -n ledger get pod invoice-api -o wide after an approved matching label or selector correction and confirm a suitable node.
  2. Run kubectl get node worker-c and confirm Ready after restoring the actual failed component.
  3. Verify the node returns to Ready without MemoryPressure and Pod eviction rates stabilize after capacity planning.
  4. Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes.

Explanation

Correct Answer

D:

Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes. This validates the affected object or control path, rather than only showing a command completed. The underlying reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The intended action is: Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages.

Incorrect Answers

A:

The alternative verifies a different condition: A hard nodeSelector must match a Node's actual labels; tolerations or Service routing cannot bypass the unmatched selector. The current case instead depends on: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Confirm that “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

B:

That test belongs to another control with this basis: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. In the current case, the governing reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The direct verification is “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

C:

This observation would test a different technical dependency: Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status. The current issue is governed by: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Its decisive check is “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

Question 1096

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: Node worker-a is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Maintain image garbage-collection, log rotation and filesystem capacity alerts.
  2. Test kubelet/CRI version compatibility and runtime services in node patch runbooks.
  3. Validate DaemonSet coverage whenever node labels, pools or taints change.
  4. Monitor peak memory with appropriate safety margins and evaluate application leaks rather than unlimited resource increases.

Explanation

Correct Answer

A:

Maintain image garbage-collection, log rotation and filesystem capacity alerts. This reduces recurrence of the specific operational failure. It reinforces the action: Run kubectl describe node worker-a and on the host inspect df -h and kubelet eviction messages. The relevant mechanism is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

Incorrect Answers

B:

This routine guards against another mechanism: Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime. The recurring exposure in the current case is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The matching preventive check is “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.

C:

The suggested review tracks a different technical risk: DaemonSets schedule according to node selection and toleration rules; unavailable node agents can break networking or logging on selected workers. Here the sustained control must address: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Choose “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.

D:

That safeguard is relevant to: The container runtime can terminate a container when it exceeds its memory cgroup limit; increasing CPU requests does not repair a memory limit failure. The current failure instead follows from: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Its prevention measure is “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.

Question 1097

At Horizon observability, in a disposable simulated environment during a monitored release window: Node worker-b is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which command or initial safe diagnostic step best addresses the stated lab task?

  1. On worker-b, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-b from an admin context.
  2. Run kubectl -n observability describe pod metrics-api and compare Pod tolerations with kubectl describe node worker-b taints.
  3. Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages.
  4. On worker-c, run sudo systemctl status kubelet and sudo journalctl -u kubelet -n 80 --no-pager along with kubectl describe node worker-c from an admin context.

Explanation

Correct Answer

C:

Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages. This is the appropriate command-line action or first safe diagnostic. The technical reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Validate the result with: Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes. Execute changes only in an authorized disposable lab or after normal production change controls.

Incorrect Answers

A:

This alternative is a response to another technical cause: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. The reported case instead depends on: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The action that directly addresses it is “Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages”.

B:

The proposed action belongs to the mechanism: A NoSchedule taint excludes Pods lacking a matching toleration even if the Node has free resources. Here the stated fault follows a different mechanism: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The corrective choice is “Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages”.

D:

This alternative is a response to another technical cause: Node readiness depends on the kubelet and node conditions; application restarts cannot fix a stopped kubelet service. The reported case instead depends on: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The action that directly addresses it is “Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages”.

Question 1098

At Horizon observability, in a disposable simulated environment during a monitored release window: Node worker-b is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. Finalizers protect cleanup work; forcibly removing them without identifying failing controllers can orphan resources and leak infrastructure.
  2. Kubernetes network connectivity requires a functioning CNI implementation on nodes; core API objects alone do not create functional Pod networking.
  3. Kubeadm control-plane components are commonly static Pods managed by kubelet; kubectl cannot diagnose them if the API server itself is unavailable.
  4. DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

Explanation

Correct Answer

D:

DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. This explains the stated condition. The corresponding action is: Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages. The direct acceptance evidence is: Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes.

Incorrect Answers

A:

Finalizers protect cleanup work; forcibly removing them without identifying failing controllers can orphan resources and leak infrastructure. That explanation applies to a different technical failure. Here the controlling principle is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

B:

The offered statement is true for this other mechanism: Kubernetes network connectivity requires a functioning CNI implementation on nodes; core API objects alone do not create functional Pod networking. It does not explain the facts described here, which follow from: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

C:

This principle describes a separate cause: Kubeadm control-plane components are commonly static Pods managed by kubelet; kubectl cannot diagnose them if the API server itself is unavailable. The reported condition is governed by: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The two mechanisms should not be conflated.

Question 1099

At Horizon observability, in a disposable simulated environment during a monitored release window: Node worker-b is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which observation best verifies the requested outcome rather than an unrelated state?

  1. Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes.
  2. Verify the node returns to Ready without MemoryPressure and Pod eviction rates stabilize after capacity planning.
  3. Confirm the CRI service is healthy and kubectl get node worker-c returns Ready after supported recovery.
  4. Run kubectl -n observability get pvc metrics-data and confirm Bound with capacity and the intended StorageClass after correction.

Explanation

Correct Answer

A:

Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes. This validates the affected object or control path, rather than only showing a command completed. The underlying reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The intended action is: Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages.

Incorrect Answers

B:

This observation would test a different technical dependency: Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status. The current issue is governed by: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Its decisive check is “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

C:

The alternative verifies a different condition: Kubelet relies on a CRI-compatible runtime for container creation; restarting application Pods cannot repair an unavailable runtime. The current case instead depends on: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Confirm that “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

D:

That test belongs to another control with this basis: A PVC binds only to compatible available storage or successful dynamic provisioning; absent provisioners, quotas or delayed binding can leave it Pending. In the current case, the governing reason is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The direct verification is “Verify node disk pressure clears and relevant Pods become stable after approved cleanup or capacity changes”.

Question 1100

At Horizon observability, in a disposable simulated environment during a monitored release window: Node worker-b is under DiskPressure and new Pods are evicted; avoid deleting arbitrary container runtime directories. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Baseline memory requests and track node allocatable resources and eviction signals.
  2. Maintain image garbage-collection, log rotation and filesystem capacity alerts.
  3. Keep cleanup controllers healthy and document controlled escalation for stuck finalizers.
  4. Review sizing and autoscaling headroom against known resource requests.

Explanation

Correct Answer

B:

Maintain image garbage-collection, log rotation and filesystem capacity alerts. This reduces recurrence of the specific operational failure. It reinforces the action: Run kubectl describe node worker-b and on the host inspect df -h and kubelet eviction messages. The relevant mechanism is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads.

Incorrect Answers

A:

This routine guards against another mechanism: Node memory pressure can cause evictions and scheduling instability independent of one container's OOMKilled status. The recurring exposure in the current case is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The matching preventive check is “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.

C:

That safeguard is relevant to: Finalizers protect cleanup work; forcibly removing them without identifying failing controllers can orphan resources and leak infrastructure. The current failure instead follows from: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. Its prevention measure is “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.

D:

This routine guards against another mechanism: The scheduler places Pods by requested resources and constraints, not the current shell's observed free CPU alone. The recurring exposure in the current case is: DiskPressure is a node condition triggered by storage thresholds; deleting live runtime data blindly can corrupt workloads. The matching preventive check is “Maintain image garbage-collection, log rotation and filesystem capacity alerts”.