Quick Answer
The answer is AWS Compute Optimizer. This managed service uses machine learning to analyze historical utilization data for CPU, memory, network, and storage, then generates rightsizing recommendations for EC2 instances by identifying over-provisioned resources and suggesting instance types that better match actual workload patterns. On the SAA-C03 exam, this question tests your ability to distinguish between AWS-native optimization tools—a common trap is confusing Compute Optimizer with Cost Explorer or Trusted Advisor, but only Compute Optimizer provides granular, ML-driven rightsizing recommendations specifically for EC2. Remember the key differentiator: if the scenario mentions “oversized instances based on utilization metrics” and a “managed AWS-native control,” your answer is always Compute Optimizer. A useful memory tip is to think of it as the “rightsizing engine” that crunches utilization history to right-size your fleet.
This SAA-C03 practice question tests your understanding of design cost-optimized architectures. 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 test environment has EC2 instances that are oversized based on CPU, memory, and network utilisation. Which AWS service should identify rightsizing recommendations? The architecture review board prefers a managed AWS-native control.
AWS DataSync
Why wrong: DataSync transfers data and does not recommend compute sizes.
AWS Shield
Why wrong: Shield protects against DDoS and does not analyse instance sizing.
AWS Artifact
Why wrong: Artifact provides compliance reports and agreements, not rightsizing recommendations.
AWS Compute Optimizer
Compute Optimizer analyses utilisation metrics and recommends rightsizing for supported resources.
Answer choices
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 a managed service that uses machine learning to analyze historical utilization metrics (CPU, memory, network, and storage) and provides rightsizing recommendations for EC2 instances. It identifies over-provisioned resources and suggests instance types that better match workload requirements, directly addressing the oversized EC2 instances in the test environment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS DataSync
Why it's wrong here
DataSync transfers data and does not recommend compute sizes.
AWS Shield
Why it's wrong here
Shield protects against DDoS and does not analyse instance sizing.
AWS Artifact
Why it's wrong here
Artifact provides compliance reports and agreements, not rightsizing recommendations.
AWS Compute Optimizer
Why this is correct
Compute Optimizer analyses utilisation metrics and recommends rightsizing for supported resources.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may confuse AWS Compute Optimizer with other monitoring or cost tools (like AWS Trusted Advisor or Cost Explorer), but the question specifically asks for a managed AWS-native service that identifies rightsizing recommendations, which is Compute Optimizer's primary function.
Detailed technical explanation
AWS Compute Optimizer ingests CloudWatch metrics (e.g., CPUUtilization, MemoryUtilization, NetworkIn/Out) over a lookback period of up to 14 days and applies machine learning models to generate recommendations. It also considers instance family, EBS volume type, and pricing models (e.g., Reserved Instances, Savings Plans) to provide actionable insights. In a real-world scenario, an organization with fluctuating workloads might use Compute Optimizer to consolidate instances and reduce costs by up to 20-30% without performance degradation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Design Cost-Optimized Architectures — This question tests Design Cost-Optimized Architectures — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Compute Optimizer — AWS Compute Optimizer is a managed service that uses machine learning to analyze historical utilization metrics (CPU, memory, network, and storage) and provides rightsizing recommendations for EC2 instances. It identifies over-provisioned resources and suggests instance types that better match workload requirements, directly addressing the oversized EC2 instances in the test environment.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A test environment has EC2 instances that are oversized based on CPU, memory, and network utilisation. Which AWS service should identify rightsizing recommendations? The design must avoid adding custom operational scripts.
mediumWhy D: AWS Compute Optimizer uses machine learning to analyze historical utilization metrics (CPU, memory, network) and generates rightsizing recommendations for EC2 instances, including instance type changes and downsizing opportunities. It operates without requiring custom scripts, as it leverages existing CloudWatch metrics and optionally the Compute Optimizer agent for enhanced memory and disk metrics.
Variation 2. A test environment has EC2 instances that are oversized based on CPU, memory, and network utilisation. Which AWS service should identify rightsizing recommendations?
mediumWhy D: AWS Compute Optimizer uses machine learning to analyze historical utilization metrics (CPU, memory, network, and storage) and provides rightsizing recommendations for EC2 instances, including over-provisioned resources. It helps reduce costs by suggesting instance types or sizes that better match actual workload demands.
Variation 3. A marketing site has EC2 instances that are oversized based on CPU, memory, and network utilisation. Which AWS service should identify rightsizing recommendations?
mediumWhy B: AWS Compute Optimizer analyzes historical utilization metrics (CPU, memory, network, and storage) from CloudWatch and uses machine learning to identify over-provisioned or under-provisioned EC2 instances. It generates actionable rightsizing recommendations, including instance type changes, to optimize cost and performance. This directly addresses the scenario of oversized EC2 instances.
Variation 4. A marketing site has EC2 instances that are oversized based on CPU, memory, and network utilisation. Which AWS service should identify rightsizing recommendations? The design must avoid adding custom operational scripts.
mediumWhy B: AWS Compute Optimizer analyzes historical utilization metrics (CPU, memory, network) of EC2 instances and generates rightsizing recommendations to reduce cost without compromising performance. It uses machine learning to identify over-provisioned resources and suggests instance type changes, all without requiring custom scripts or agents.
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Last reviewed: Jun 11, 2026
This SAA-C03 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 SAA-C03 exam.
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