Question 254 of 500
Deploying and implementing a cloud solutionmediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is GKE Autopilot, because it directly meets the team’s requirements for minimized management overhead, automatic right-sizing, and per-Pod billing. Unlike Standard mode, where you must manually manage node pools and pay for the underlying VMs regardless of utilization, Autopilot abstracts away the nodes entirely: it automatically provisions and right-sizes the infrastructure based on each Pod’s CPU and memory requests, and you are billed only for those requested resources, not the nodes themselves. On the Google Associate Cloud Engineer exam, this distinction tests your understanding of operational trade-offs—Autopilot is the “hands-off” choice for teams that want to focus on workloads, while Standard gives you full control over node configuration. A common trap is assuming Standard can offer per-Pod billing, but it cannot; Standard bills for the entire node. Memory tip: “Auto” stands for automated nodes, automatic sizing, and automated billing—three “A’s” that align with the team’s goals.

Google ACE Deploying and implementing a cloud solution Practice Question

This ACE practice question tests your understanding of deploying and implementing a cloud solution. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A GKE team is comparing Autopilot and Standard cluster modes for a new project. They want to minimize infrastructure management overhead, automatically right-size node resources, and be billed only for Pod resource requests. Which mode matches these requirements?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "minimum / minimize"

    Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1mediummultiple choice
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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

GKE Autopilot — managed nodes, automatic right-sizing, and per-Pod billing

GKE Autopilot is the correct choice because it fully manages the underlying node infrastructure, automatically right-sizes node resources based on Pod resource requests, and bills only for the requested CPU and memory of Pods, not the underlying nodes. This aligns directly with the team's goals of minimizing management overhead, automatic right-sizing, and per-Pod billing.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • GKE Standard — it provides more control over node configuration

    Why it's wrong here

    GKE Standard requires managing node pools, machine types, and autoscaler settings — the opposite of minimal management overhead.

  • GKE Autopilot — managed nodes, automatic right-sizing, and per-Pod billing

    Why this is correct

    Autopilot removes all node management: Google provisions, scales, and optimizes nodes automatically. Billing is based on Pod resource requests — precisely matching the described requirements.

    Clue confirmation

    The clue word "minimum / minimize" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • GKE Standard with cluster autoscaler and node auto-provisioning enabled

    Why it's wrong here

    Even with autoscaling enabled, Standard mode requires configuring machine families, node pool settings, and monitoring — more overhead than Autopilot.

  • Both modes are equivalent in management overhead — Autopilot is just a pricing model

    Why it's wrong here

    Autopilot fundamentally removes node-level management responsibility from teams — it's not just a pricing change.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the misconception that GKE Standard with autoscaling features provides the same per-Pod billing and zero node management as Autopilot, but the key difference is that Standard always bills for the underlying nodes, not the Pods.

Detailed technical explanation

How to think about this question

Under the hood, GKE Autopilot uses a control plane that automatically selects node types and sizes based on Pod resource requests and constraints, leveraging the same Kubernetes scheduler but with additional node lifecycle management. A subtle behavior is that Autopilot enforces resource requests and limits for all Pods, and any Pod without explicit requests will be assigned default values, which can lead to unexpected costs if not properly configured. In a real-world scenario, a team migrating a stateful workload to Autopilot must ensure Pods have appropriate resource requests to avoid over-provisioning or under-provisioning, as the node is automatically scaled to match the sum of requests.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this ACE question test?

Deploying and implementing a cloud solution — This question tests Deploying and implementing a cloud solution — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: GKE Autopilot — managed nodes, automatic right-sizing, and per-Pod billing — GKE Autopilot is the correct choice because it fully manages the underlying node infrastructure, automatically right-sizes node resources based on Pod resource requests, and bills only for the requested CPU and memory of Pods, not the underlying nodes. This aligns directly with the team's goals of minimizing management overhead, automatic right-sizing, and per-Pod billing.

What should I do if I get this ACE question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 30, 2026

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This ACE practice question is part of Courseiva's free Google Cloud 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 ACE exam.