- A
Cloud Monitoring — set alerts for low CPU utilization.
Why wrong: Cloud Monitoring can alert on low CPU but doesn't generate specific rightsizing recommendations. Active Assist's Recommender analyzes usage patterns and recommends specific machine type changes.
- B
Active Assist Recommender — ML-based VM rightsizing recommendations.
Active Assist analyzes historical VM utilization and recommends specific machine type downgrades (e.g., n2-standard-8 → n2-standard-4) with projected savings. Available in the console and via API.
- C
Cloud Asset Inventory — lists all VMs and their configurations.
Why wrong: Cloud Asset Inventory catalogs resource configurations but doesn't analyze utilization patterns or generate cost optimization recommendations.
- D
Cloud Billing budgets — set spending limits to prevent overspend.
Why wrong: Billing budgets alert when spending thresholds are approached but don't identify specific underutilized resources or recommend configuration changes.
Quick Answer
The answer is the Active Assist Recommender, which provides ML-based VM rightsizing recommendations. This tool is correct because it analyzes actual CPU and memory utilization over the past eight days to identify underutilized Compute Engine VMs—those with high provisioned capacity but low actual usage—and suggests optimal machine types to reduce costs. On the Google Cloud Digital Leader exam, this question tests your understanding of how Active Assist proactively delivers cost optimization insights, often appearing alongside topics like idle VM recommendations and committed use discounts. A common trap is confusing Active Assist with the Pricing Calculator or Cost Management tools, but remember that Active Assist is specifically for actionable, ML-driven recommendations based on observed usage patterns. For a quick memory tip, think “Active Assist actively analyzes actual usage” to distinguish it from static cost estimation tools.
Cloud Digital Leader Scaling with Google Cloud operations Practice Question
This GCDL practice question tests your understanding of scaling with google cloud operations. 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 company wants to proactively identify underutilized Compute Engine VMs (high provisioned capacity but low actual usage) to reduce costs. Which Google Cloud tool provides recommendations for right-sizing VMs?
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
Active Assist Recommender — ML-based VM rightsizing recommendations.
Google Cloud's Active Assist provides intelligent recommendations including VM rightsizing recommendations. These are powered by ML analysis of actual VM CPU and memory utilization over the past 8 days. The recommendations appear in the Cloud Console (Compute Engine → VM instances → Recommendations) and in the Recommender API. Rightsizing recommendations suggest optimal machine types based on observed usage, often identifying VMs that can be downsized to save significant costs.
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.
- ✗
Cloud Monitoring — set alerts for low CPU utilization.
Why it's wrong here
Cloud Monitoring can alert on low CPU but doesn't generate specific rightsizing recommendations. Active Assist's Recommender analyzes usage patterns and recommends specific machine type changes.
- ✓
Active Assist Recommender — ML-based VM rightsizing recommendations.
Why this is correct
Active Assist analyzes historical VM utilization and recommends specific machine type downgrades (e.g., n2-standard-8 → n2-standard-4) with projected savings. Available in the console and via API.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Asset Inventory — lists all VMs and their configurations.
Why it's wrong here
Cloud Asset Inventory catalogs resource configurations but doesn't analyze utilization patterns or generate cost optimization recommendations.
- ✗
Cloud Billing budgets — set spending limits to prevent overspend.
Why it's wrong here
Billing budgets alert when spending thresholds are approached but don't identify specific underutilized resources or recommend configuration changes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 GCDL exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Scaling with Google Cloud operations — study guide chapter
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FAQ
Questions learners often ask
What does this GCDL question test?
Scaling with Google Cloud operations — This question tests Scaling with Google Cloud operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Active Assist Recommender — ML-based VM rightsizing recommendations. — Google Cloud's Active Assist provides intelligent recommendations including VM rightsizing recommendations. These are powered by ML analysis of actual VM CPU and memory utilization over the past 8 days. The recommendations appear in the Cloud Console (Compute Engine → VM instances → Recommendations) and in the Recommender API. Rightsizing recommendations suggest optimal machine types based on observed usage, often identifying VMs that can be downsized to save significant costs.
What should I do if I get this GCDL question wrong?
Identify which GCDL exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: May 19, 2026
This GCDL 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 GCDL exam.
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