CKAD Application Design and Build Practice Question
You are tasked with running a batch job that processes 100 items in parallel, using a Kubernetes Job. The Job should ensure that all items are processed even if some pods fail, and the total number of pod failures should be limited to 3. Which Job configuration is correct?
⚠ Common exam trap
A common mix-up: candidates confuse `backoffLimit` (which limits pod failures) with `activeDeadlineSeconds` (which limits the overall Job runtime), leading candidates to pick Option D, which fails to cap failures and instead imposes a time constraint.
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
✓
Set spec.parallelism: 100, spec.completions: 100, spec.backoffLimit: 3
Setting `spec.parallelism: 100` allows 100 pods to run concurrently, `spec.completions: 100` ensures all 100 items are processed (each pod handles one item), and `spec.backoffLimit: 3` limits the total number of pod failures to 3 before the Job is marked as failed. This configuration guarantees that even if some pods fail, the Job will retry them up to the specified backoff limit, ensuring all items are processed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set spec.parallelism: 100, spec.completions: 100, spec.backoffLimit: 3
Why this is correct
This configuration correctly matches the workload: spec.parallelism: 100 lets up to 100 pods run simultaneously to process the 100 items in parallel, while spec.completions: 100 ensures the Job is not marked successful until each of the 100 items is handled by a successful pod completion. Adding spec.backoffLimit: 3 caps the number of retries for failing pods to 3, providing a sane bound on wasted work. Together these fields encode the exact concurrency and completion requirements for a 100-item batch without any time-based preemption.
- ✗
Set spec.parallelism: 1, spec.completions: 100, spec.backoffLimit: 3
Why it's wrong here
Setting spec.parallelism: 1 defeats the stated requirement to run the batch job with high concurrency. Even though spec.completions: 100 still guarantees all 100 items are processed, pods run strictly one at a time, so the workload is effectively serialized. This could be acceptable for low-volume jobs, but for 100 items it is likely far too slow and underutilizes the cluster; the problem explicitly asks for parallelism, so this option fails the core condition.
- ✗
Set spec.parallelism: 100, spec.completions: 1, spec.backoffLimit: 3
Why it's wrong here
The critical flaw here is spec.completions: 1. With parallelism 100, multiple pods can start at once, but the Job terminates as soon as a single pod completes successfully, because the completion count of 1 is satisfied. That means only one item out of 100 is processed before the Job is considered done; the remaining 99 items are never touched. The correct completion count must equal the total number of items, not the concurrency level.
- ✗
Set spec.parallelism: 100, spec.completions: 100, spec.activeDeadlineSeconds: 300
Why it's wrong here
activeDeadlineSeconds imposes a hard time limit on the entire Job, not a retry limit for individual failed pods. If the Job does not finish within 300 seconds, Kubernetes forcibly terminates the Job and marks it failed, even if the pods would otherwise succeed. The requested backoffLimit is meant to control how many times a failing pod is restarted or re-created; activeDeadlineSeconds cannot replace that mechanism and could cause premature termination under heavy load.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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