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CNCF CKA Practice Test 35

Question 340

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: Deployment invoice-api in namespace ledger was just updated with a faulty Pod template and must return to its prior revision. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Control privileged node labels and review availability before adding hard scheduling constraints.
  2. Track HPA metric availability and avoid simultaneous manual scaling policies that fight the controller.
  3. Check disruption budgets and readiness behavior before application restarts.
  4. Retain meaningful revisions and perform health checks after every rollback.

Explanation

Correct Answer

D:

Retain meaningful revisions and perform health checks after every rollback. This reduces recurrence of the specific operational failure. It reinforces the action: Run kubectl -n ledger rollout undo deployment/invoice-api after checking rollout history. The relevant mechanism is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Incorrect Answers

A:

That safeguard is relevant to: Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. The current failure instead follows from: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Its prevention measure is “Retain meaningful revisions and perform health checks after every rollback”.

B:

This routine guards against another mechanism: HPA evaluates metrics relative to Pod resource requests; missing metrics or requests can leave desired replicas unknown. The recurring exposure in the current case is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The matching preventive check is “Retain meaningful revisions and perform health checks after every rollback”.

C:

The suggested review tracks a different technical risk: Rollout restart annotates the Pod template so the Deployment controller replaces Pods according to its rollout strategy. Here the sustained control must address: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Choose “Retain meaningful revisions and perform health checks after every rollback”.

Question 341

At Horizon observability, in a disposable simulated environment during a monitored release window: Deployment metrics-api in namespace observability was just updated with a faulty Pod template and must return to its prior revision. 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. Run kubectl -n fulfillment rollout status deployment/dispatch-api --timeout=120s.
  2. Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history.
  3. Run kubectl -n fulfillment patch deployment dispatch-api --type=merge -p '{"spec":{"template":{"spec":{"nodeSelector":{"workload":"restricted"}}}}}'.
  4. Run kubectl -n ledger rollout restart deployment/invoice-api.

Explanation

Correct Answer

B:

Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history. This is the appropriate command-line action or first safe diagnostic. The technical reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Validate the result with: Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration. 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: A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress. The reported case instead depends on: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The action that directly addresses it is “Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history”.

C:

The offered action targets: Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. The case facts point instead to: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Implement “Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history” to address that specific condition.

D:

This alternative is a response to another technical cause: Rollout restart annotates the Pod template so the Deployment controller replaces Pods according to its rollout strategy. The reported case instead depends on: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The action that directly addresses it is “Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history”.

Question 342

At Horizon observability, in a disposable simulated environment during a monitored release window: Deployment metrics-api in namespace observability was just updated with a faulty Pod template and must return to its prior revision. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout.
  2. A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress.
  3. A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.
  4. A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity.

Explanation

Correct Answer

C:

A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. This explains the stated condition. The corresponding action is: Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history. The direct acceptance evidence is: Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration.

Incorrect Answers

A:

Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. That explanation applies to a different technical failure. Here the controlling principle is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

B:

The offered statement is true for this other mechanism: A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress. It does not explain the facts described here, which follow from: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

D:

A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity. That explanation applies to a different technical failure. Here the controlling principle is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Question 343

At Horizon observability, in a disposable simulated environment during a monitored release window: Deployment metrics-api in namespace observability was just updated with a faulty Pod template and must return to its prior revision. 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 deployment invoice-api and confirm updated and available replicas match the desired count.
  2. Run kubectl -n fulfillment get deployment dispatch-api and confirm updated and available replicas match the desired count.
  3. Run kubectl -n fulfillment get deployment dispatch-api -o jsonpath='{.spec.template.spec.nodeSelector}' and check Pod placements.
  4. Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration.

Explanation

Correct Answer

D:

Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration. This validates the affected object or control path, rather than only showing a command completed. The underlying reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The intended action is: Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history.

Incorrect Answers

A:

That test belongs to another control with this basis: A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress. In the current case, the governing reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The direct verification is “Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration”.

B:

This observation would test a different technical dependency: A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress. The current issue is governed by: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Its decisive check is “Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration”.

C:

The alternative verifies a different condition: Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. The current case instead depends on: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Confirm that “Run kubectl -n observability rollout status deployment/metrics-api and inspect the restored image and configuration”.

Question 344

At Horizon observability, in a disposable simulated environment during a monitored release window: Deployment metrics-api in namespace observability was just updated with a faulty Pod template and must return to its prior revision. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Retain meaningful revisions and perform health checks after every rollback.
  2. Treat taints and tolerations as paired scheduling controls and review preexisting tolerations when patching arrays.
  3. Control privileged node labels and review availability before adding hard scheduling constraints.
  4. Review resource sizing against actual usage and avoid scheduling on unsupported limits alone.

Explanation

Correct Answer

A:

Retain meaningful revisions and perform health checks after every rollback. This reduces recurrence of the specific operational failure. It reinforces the action: Run kubectl -n observability rollout undo deployment/metrics-api after checking rollout history. The relevant mechanism is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Incorrect Answers

B:

The suggested review tracks a different technical risk: A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity. Here the sustained control must address: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Choose “Retain meaningful revisions and perform health checks after every rollback”.

C:

That safeguard is relevant to: Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. The current failure instead follows from: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Its prevention measure is “Retain meaningful revisions and perform health checks after every rollback”.

D:

This routine guards against another mechanism: Requests inform scheduling and limits cap runtime consumption; the settings play different roles. The recurring exposure in the current case is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The matching preventive check is “Retain meaningful revisions and perform health checks after every rollback”.

Question 345

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Deployment dispatch-api in namespace fulfillment was just updated with a faulty Pod template and must return to its prior revision. 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. Run kubectl -n fulfillment set image deployment/dispatch-api app=registry.example.test/dispatch-api:v4.
  2. Run kubectl -n observability rollout restart deployment/metrics-api.
  3. Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history.
  4. Run kubectl -n fulfillment patch deployment dispatch-api --type=merge -p '{"spec":{"template":{"spec":{"tolerations":[{"key":"special","operator":"Equal","value":"dedicated","effect":"NoSchedule"}]}}}}' after checking existing tolerations.

Explanation

Correct Answer

C:

Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history. This is the appropriate command-line action or first safe diagnostic. The technical reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Validate the result with: Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration. Execute changes only in an authorized disposable lab or after normal production change controls.

Incorrect Answers

A:

The proposed action belongs to the mechanism: Updating a Deployment Pod template triggers a controlled rollout; restarting Pods alone will not change the image reference. Here the stated fault follows a different mechanism: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The corrective choice is “Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history”.

B:

The offered action targets: Rollout restart annotates the Pod template so the Deployment controller replaces Pods according to its rollout strategy. The case facts point instead to: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Implement “Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history” to address that specific condition.

D:

The proposed action belongs to the mechanism: A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity. Here the stated fault follows a different mechanism: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The corrective choice is “Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history”.

Question 346

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Deployment dispatch-api in namespace fulfillment was just updated with a faulty Pod template and must return to its prior revision. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. Pod templates can reference Secret keys without copying literal secret values into the Deployment spec.
  2. ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods.
  3. Deployment rolling-update parameters govern how many old Pods may be unavailable and extra Pods may be created during replacement.
  4. A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Explanation

Correct Answer

D:

A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. This explains the stated condition. The corresponding action is: Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history. The direct acceptance evidence is: Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration.

Incorrect Answers

A:

The offered statement is true for this other mechanism: Pod templates can reference Secret keys without copying literal secret values into the Deployment spec. It does not explain the facts described here, which follow from: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

B:

This principle describes a separate cause: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. The reported condition is governed by: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The two mechanisms should not be conflated.

C:

Deployment rolling-update parameters govern how many old Pods may be unavailable and extra Pods may be created during replacement. That explanation applies to a different technical failure. Here the controlling principle is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Question 347

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Deployment dispatch-api in namespace fulfillment was just updated with a faulty Pod template and must return to its prior revision. 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 fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration.
  2. Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect new Pod creation timestamps.
  3. Run kubectl -n fulfillment get deployment dispatch-api -o yaml and inspect spec.template.spec.tolerations before testing scheduling.
  4. Run kubectl -n observability get deployment metrics-api and confirm updated and available replicas match the desired count.

Explanation

Correct Answer

A:

Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration. This validates the affected object or control path, rather than only showing a command completed. The underlying reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The intended action is: Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history.

Incorrect Answers

B:

The alternative verifies a different condition: Rollout restart annotates the Pod template so the Deployment controller replaces Pods according to its rollout strategy. The current case instead depends on: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Confirm that “Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration”.

C:

That test belongs to another control with this basis: A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity. In the current case, the governing reason is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The direct verification is “Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration”.

D:

This observation would test a different technical dependency: A successful API mutation does not guarantee updated Pods are healthy and available; rollout status tracks controller progress. The current issue is governed by: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Its decisive check is “Run kubectl -n fulfillment rollout status deployment/dispatch-api and inspect the restored image and configuration”.

Question 348

At Pinecrest logistics, in a sandbox Kubernetes cluster during a recovery tabletop exercise: Deployment dispatch-api in namespace fulfillment was just updated with a faulty Pod template and must return to its prior revision. Work only within the stated target and verify before declaring success. Which operational control most directly prevents this failure or configuration drift?

  1. Control privileged node labels and review availability before adding hard scheduling constraints.
  2. Retain meaningful revisions and perform health checks after every rollback.
  3. Check disruption budgets and readiness behavior before application restarts.
  4. Pin reviewed image tags or digests and retain rollout history for recovery.

Explanation

Correct Answer

B:

Retain meaningful revisions and perform health checks after every rollback. This reduces recurrence of the specific operational failure. It reinforces the action: Run kubectl -n fulfillment rollout undo deployment/dispatch-api after checking rollout history. The relevant mechanism is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template.

Incorrect Answers

A:

The suggested review tracks a different technical risk: Node selectors constrain scheduling to nodes whose labels match all specified key/value pairs; patching the Pod template triggers a rollout. Here the sustained control must address: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Choose “Retain meaningful revisions and perform health checks after every rollback”.

C:

This routine guards against another mechanism: Rollout restart annotates the Pod template so the Deployment controller replaces Pods according to its rollout strategy. The recurring exposure in the current case is: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. The matching preventive check is “Retain meaningful revisions and perform health checks after every rollback”.

D:

The suggested review tracks a different technical risk: Updating a Deployment Pod template triggers a controlled rollout; restarting Pods alone will not change the image reference. Here the sustained control must address: A Deployment tracks ReplicaSet revisions and rollout undo restores a recorded earlier Pod template. Choose “Retain meaningful revisions and perform health checks after every rollback”.

Question 349

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: An existing workload invoice-api in namespace ledger requires a ConfigMap invoice-config containing the nonsecret setting MODE=staging. 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. Run kubectl -n ledger patch deployment invoice-api --type=merge -p '{"spec":{"template":{"spec":{"tolerations":[{"key":"special","operator":"Equal","value":"dedicated","effect":"NoSchedule"}]}}}}' after checking existing tolerations.
  2. Run kubectl -n fulfillment set env deployment/dispatch-api --from=secret/lab-dispatch-key.
  3. Run kubectl -n ledger autoscale deployment invoice-api --cpu-percent=65 --min=2 --max=6 after setting valid CPU requests.
  4. Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists.

Explanation

Correct Answer

D:

Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists. This is the appropriate command-line action or first safe diagnostic. The technical reason is: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. Validate the result with: Run kubectl -n ledger get configmap invoice-config -o jsonpath='{.data.MODE}' and confirm the declared value. Execute changes only in an authorized disposable lab or after normal production change controls.

Incorrect Answers

A:

The offered action targets: A toleration allows a Pod past a matching taint but does not ensure placement without node selection and capacity. The case facts point instead to: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. Implement “Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists” to address that specific condition.

B:

This alternative is a response to another technical cause: Pod templates can reference Secret keys without copying literal secret values into the Deployment spec. The reported case instead depends on: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. The action that directly addresses it is “Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists”.

C:

The proposed action belongs to the mechanism: HPA evaluates metrics relative to Pod resource requests; missing metrics or requests can leave desired replicas unknown. Here the stated fault follows a different mechanism: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. The corrective choice is “Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists”.

Question 350

At Northstar finance, in an isolated training cluster during a scheduled maintenance rehearsal: An existing workload invoice-api in namespace ledger requires a ConfigMap invoice-config containing the nonsecret setting MODE=staging. Work only within the stated target and verify before declaring success. Which Kubernetes behavior best explains the required change or diagnosis?

  1. ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods.
  2. Environment variables are copied into new container processes from ConfigMap values; existing running processes need replacement to pick up changes.
  3. Pod templates can reference Secret keys without copying literal secret values into the Deployment spec.
  4. Updating a Deployment Pod template triggers a controlled rollout; restarting Pods alone will not change the image reference.

Explanation

Correct Answer

A:

ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. This explains the stated condition. The corresponding action is: Run kubectl -n ledger create configmap invoice-config --from-literal=MODE=staging after checking whether it already exists. The direct acceptance evidence is: Run kubectl -n ledger get configmap invoice-config -o jsonpath='{.data.MODE}' and confirm the declared value.

Incorrect Answers

B:

Environment variables are copied into new container processes from ConfigMap values; existing running processes need replacement to pick up changes. That explanation applies to a different technical failure. Here the controlling principle is: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods.

C:

The offered statement is true for this other mechanism: Pod templates can reference Secret keys without copying literal secret values into the Deployment spec. It does not explain the facts described here, which follow from: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods.

D:

This principle describes a separate cause: Updating a Deployment Pod template triggers a controlled rollout; restarting Pods alone will not change the image reference. The reported condition is governed by: ConfigMaps hold ordinary configuration data; their existence does not automatically inject values into running Pods. The two mechanisms should not be conflated.