- A
AWS Trusted Advisor
Why wrong: AWS Trusted Advisor provides cost optimization checks that include identifying idle instances and instances with low utilization, but it does not use machine learning to provide detailed right-sizing recommendations based on 30-day utilization patterns. Its recommendations are based on simple thresholds (e.g., CPU utilization less than 10% for 14 days) rather than comprehensive ML analysis.
- B
AWS Cost Explorer
Why wrong: AWS Cost Explorer is a tool for visualizing, understanding, and managing AWS costs and usage over time. It provides cost and usage graphs, forecasting, and resource-level granularity, but it does not generate right-sizing recommendations for EC2 instances. It is primarily a cost analysis tool, not an optimization recommendation engine.
- C
AWS Compute Optimizer
AWS Compute Optimizer is a service that uses machine learning to analyze historical utilization metrics (CPU, memory, network throughput) for EC2 instances and other resources. It identifies over- and under-provisioned instances and provides specific recommendations to change instance types or sizes to reduce costs or improve performance. This directly matches the finance team's requirement for ML-based right-sizing based on 30-day utilization data.
- D
AWS Budgets
Why wrong: AWS Budgets is a service that allows you to set custom spending thresholds and receive alerts when costs or usage exceed (or are forecast to exceed) the budgeted amount. It does not analyze instance utilization or provide recommendations for resizing instances. Its purpose is cost tracking and alerting, not optimization.
Quick Answer
The answer is AWS Compute Optimizer. This managed service is the correct choice because it uses machine learning to analyze historical CPU and memory utilization over up to 93 days, automatically identifying over-provisioned and under-provisioned EC2 instances and delivering actionable rightsizing recommendations to reduce compute costs. On the AWS Certified Cloud Practitioner CLF-C02 exam, this question tests your ability to match a specific business need—cost optimization through rightsizing—with the correct AWS service. A common trap is confusing Compute Optimizer with AWS Trusted Advisor, which provides general cost checks but lacks the deep, ML-driven analysis of individual instance utilization over time. Remember the key differentiator: Compute Optimizer focuses on instance-level rightsizing based on historical metrics, while Trusted Advisor offers broader cost optimization checks. A helpful memory tip is to think of Compute Optimizer as the “personal trainer” for your EC2 instances—it analyzes past performance to recommend the right size for efficiency.
CLF-C02 Billing, Pricing, and Support Practice Question
This CLF-C02 practice question tests your understanding of billing, pricing, and support. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 runs a mix of Amazon EC2 instances across multiple AWS Regions to support its e-commerce platform. The finance team wants to reduce compute costs by right-sizing resources. They need a managed tool that analyzes historical CPU and memory utilization over 30 days, uses machine learning to identify over-provisioned and under-provisioned instances, and provides actionable recommendations to adjust instance sizes. Which AWS tool should the finance team use?
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
AWS Compute Optimizer
AWS Compute Optimizer is the correct choice because it is a managed service that uses machine learning to analyze historical utilization metrics (CPU, memory, etc.) over up to 93 days, identifies over-provisioned and under-provisioned EC2 instances, and generates actionable rightsizing recommendations. The question specifically requires a tool that analyzes 30 days of historical CPU and memory data with ML-driven insights, which aligns exactly with Compute Optimizer's core functionality.
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.
- ✗
AWS Trusted Advisor
Why it's wrong here
AWS Trusted Advisor provides cost optimization checks that include identifying idle instances and instances with low utilization, but it does not use machine learning to provide detailed right-sizing recommendations based on 30-day utilization patterns. Its recommendations are based on simple thresholds (e.g., CPU utilization less than 10% for 14 days) rather than comprehensive ML analysis.
- ✗
AWS Cost Explorer
Why it's wrong here
AWS Cost Explorer is a tool for visualizing, understanding, and managing AWS costs and usage over time. It provides cost and usage graphs, forecasting, and resource-level granularity, but it does not generate right-sizing recommendations for EC2 instances. It is primarily a cost analysis tool, not an optimization recommendation engine.
- ✓
AWS Compute Optimizer
Why this is correct
AWS Compute Optimizer is a service that uses machine learning to analyze historical utilization metrics (CPU, memory, network throughput) for EC2 instances and other resources. It identifies over- and under-provisioned instances and provides specific recommendations to change instance types or sizes to reduce costs or improve performance. This directly matches the finance team's requirement for ML-based right-sizing based on 30-day utilization data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Budgets
Why it's wrong here
AWS Budgets is a service that allows you to set custom spending thresholds and receive alerts when costs or usage exceed (or are forecast to exceed) the budgeted amount. It does not analyze instance utilization or provide recommendations for resizing instances. Its purpose is cost tracking and alerting, not optimization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse AWS Cost Explorer's cost-based rightsizing recommendations (which are purely financial) with Compute Optimizer's utilization-based ML recommendations, leading them to select Cost Explorer despite the question explicitly requiring CPU and memory analysis.
Detailed technical explanation
How to think about this question
AWS Compute Optimizer ingests CloudWatch metrics (CPU, memory, network throughput, and disk I/O) and applies a machine learning model trained on millions of AWS workloads to detect patterns of over-provisioning (e.g., sustained low CPU) and under-provisioning (e.g., memory pressure). It supports not only EC2 instances but also Auto Scaling groups, EBS volumes, and Lambda functions, and can provide recommendations across multiple Regions by aggregating data from the AWS Compute Optimizer console or API. A subtle behavior is that it requires at least 30 days of historical data for EC2 recommendations, but the ML model improves with up to 93 days of data, and it will not generate recommendations for instances with less than 30 days of utilization history.
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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Billing, Pricing, and Support — study guide chapter
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FAQ
Questions learners often ask
What does this CLF-C02 question test?
Billing, Pricing, and Support — This question tests Billing, Pricing, and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Compute Optimizer — AWS Compute Optimizer is the correct choice because it is a managed service that uses machine learning to analyze historical utilization metrics (CPU, memory, etc.) over up to 93 days, identifies over-provisioned and under-provisioned EC2 instances, and generates actionable rightsizing recommendations. The question specifically requires a tool that analyzes 30 days of historical CPU and memory data with ML-driven insights, which aligns exactly with Compute Optimizer's core functionality.
What should I do if I get this CLF-C02 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This CLF-C02 practice question is part of Courseiva's free Amazon Web Services 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 CLF-C02 exam.
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