The AWS Certified Solutions Architect - Professional Exam (SAP-C02) is designed for experienced AWS professionals who design and deploy scalable, reliable, and secure solutions on Amazon Web Services. This certification validates your ability to architect complex, multi-tier applications and guide organizational strategy. This page outlines the exam structure, core topics, and effective study approaches to help you prepare confidently for the SAP-C02 assessment.
Use this topic map to guide your study for Amazon SAP-C02 (AWS Certified Solutions Architect - Professional Exam) within the Amazon Professional, AWS Certified Solutions Architect Professional path.
The SAP-C02 exam combines multiple-choice and scenario-based questions to assess both foundational knowledge and applied decision-making in real-world situations. Questions progress in difficulty and require you to balance competing architectural concerns.
Questions emphasize practical reasoning and require you to justify why one solution better meets organizational goals than alternatives.
An effective study plan maps the four core domains to weekly learning cycles, combines concept review with hands-on practice, and includes timed mock exams to build test readiness. Allocate more time to areas where your experience is weaker, and use practice questions to identify knowledge gaps early.
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Design for New Solutions and Accelerate Workload Migration and Modernization typically represent the largest portion of the exam. However, all four domains are equally important for passing, and questions often blend multiple topics into single scenarios. Focus on understanding how to apply each domain to realistic business problems rather than memorizing topic percentages.
In practice, these domains overlap. You Design for Organizational Complexity when establishing governance and multi-account strategies, then Design for New Solutions when building applications within that framework. Continuous Improvement happens as workloads run and you optimize costs and performance. Migration and Modernization often trigger re-evaluation of your organizational design and solution architecture. Understanding these connections helps you answer scenario questions that span multiple domains.
AWS recommends at least two years of hands-on experience designing distributed systems on AWS. Practical experience with compute, storage, networking, and databases is essential; you should be comfortable with the AWS Management Console, Infrastructure as Code, and multi-account architectures. If you lack depth in certain services, focus lab time on those areas and use practice questions to fill gaps.
Common errors include choosing the cheapest option without considering performance or compliance requirements, overlooking security and governance implications, and misreading scenario details. Candidates also sometimes select technically correct answers that don't best solve the stated business problem. Read each question and all options carefully, and always ask yourself which solution best balances all stated constraints.
In the final week, take one full-length timed practice exam and review results in detail. Spend remaining time re-reading explanations for questions you missed, not memorizing new content. Get adequate sleep and avoid cramming new topics; focus on reinforcing weak areas and building confidence. On exam day, manage your time by reading questions completely and flagging difficult items to revisit if time allows.
A company is planning to migrate 1,000 on-premises servers to AWS. The servers run on several VMware clusters in the company's data center. As part of the migration plan, the company wants to gather server metrics such as CPU details, RAM usage, operating system information, and running processes. The company then wants to query and analyze the data.
Which solution will meet these requirements?
it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.
A large company runs an AWS migration assessment. Now, the company must select its first workload to migrate to AWS. The company has the following goals:
* Build cloud migration skills. * Minimize business risk. * Move quickly to prepare for an on-premises data center contract expiration in less than 6 months.
Which migration approach aligns with AWS guidance for selecting and migrating a first workload?
A flood monitoring agency has deployed more than 10.000 water-level monitoring sensors. Sensors send continuous data updates, and each update is less than 1 MB in size. The agency has a fleet of on-premises application servers. These servers receive upda.es 'on the sensors, convert the raw data into a human readable format, and write the results loan on-premises relational database server. Data analysts then use simple SOL queries to monitor the data.
The agency wants to increase overall application availability and reduce the effort that is required to perform maintenance tasks These maintenance tasks, which include updates and patches to the application servers, cause downtime. While an application server is down, data is lost from sensors because the remaining servers cannot handle the entire workload.
The agency wants a solution that optimizes operational overhead and costs. A solutions architect recommends the use of AWS loT Core to collect the sensor data.
What else should the solutions architect recommend to meet these requirements?
To enhance application availability and reduce maintenance-induced downtime, sending sensor data to Amazon Kinesis Data Firehose, processing it with an AWS Lambda function, converting it to Apache Parquet format, and storing it in Amazon S3 is an effective strategy. This approach leverages serverless architectures for scalability and reliability. Data analysts can then query the optimized data using Amazon Athena, a serverless interactive query service, which supports complex queries on data stored in S3 without the need for traditional database servers, optimizing operational overhead and costs.
AWS Documentation on AWS IoT Core, Amazon Kinesis Data Firehose, AWS Lambda, Amazon S3, and Amazon Athena provides a comprehensive framework for building a scalable, serverless data processing pipeline. This solution aligns with AWS best practices for processing and analyzing large-scale data streams efficiently.
A company uses an organization in AWS Organizations to manage the company's AWS accounts. The company uses AWS CloudFormation to deploy all infrastructure. A finance team wants to buikJ a chargeback model The finance team asked each business unit to tag resources by using a predefined list of project values.
When the finance team used the AWS Cost and Usage Report in AWS Cost Explorer and filtered based on project, the team noticed noncompliant project values. The company wants to enforce the use of project tags for new resources.
Which solution will meet these requirements with the LEAST effort?
The best solution is to create a tag policy that contains the allowed project tag values in the organization's management account and create an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added. A tag policy is a type of policy that can help standardize tags across resources in the organization's accounts. A tag policy can specify the allowed tag keys, values, and case treatment for compliance. A service control policy (SCP) is a type of policy that can restrict the actions that users and roles can perform in the organization's accounts. An SCP can deny access to specific API operations unless certain conditions are met, such as having a specific tag. By creating a tag policy in the management account and attaching it to each OU, the organization can enforce consistent tagging across all accounts. By creating an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added, the organization can prevent users from creating new resources without proper tagging. This solution will meet the requirements with the least effort, as it does not involve creating additional resources or modifying existing ones.Reference:Tag policies - AWS Organizations,Service control policies - AWS Organizations,AWS CloudFormation User Guide
A company is using an Amazon ECS cluster to run a data-processing application. Different business groups share ECS services in the ECS cluster. The ECS cluster runs on Amazon EC2 instances. ECS cluster auto scaling is enabled.
The company needs to assign EC2 costs of ECS tasks to the appropriate business groups.
Which solution will meet this requirement with the LEAST operational overhead?
B is correct because the requirement is to allocate shared EC2 infrastructure costs (ECS on EC2, shared cluster) down to the business groups with the least operational effort. With shared compute, the EC2 line-item charges are not naturally per ''task,'' so you need a cost allocation feature that can split shared costs using workload metadata. Split cost allocation data combined with the AWS Cost and Usage Report (CUR) and resource tags provides the standard AWS mechanism to distribute (''split'') shared resource costs across consumers for chargeback/showback reporting. This avoids operationally heavy changes (like redesigning clusters) and supports automated reporting.
Why the other options are incorrect:
A: Tagging the Auto Scaling group can only attribute costs at the ASG level, not split costs between multiple business groups sharing the same cluster capacity. It does not solve per-business-group allocation when capacity is shared.
C: Separate clusters per group would work but is higher operational overhead (more clusters to manage, scaling policies, capacity planning, governance), which violates the ''least operational overhead'' requirement.
D: Cost Categories are useful for mapping and organizing costs, but by themselves they do not solve the underlying need to split shared EC2 costs among multiple consumers unless you already have an accurate split basis. Option B directly targets split allocation of shared costs with tagging and CUR.
AWS Cost Explorer Documentation: split cost allocation data and cost analysis capabilities
AWS Cost and Usage Report Documentation: detailed line-item reporting and tag-based cost allocation
AWS Billing and Cost Management Documentation: activating and using cost allocation tags
AWS Certified Solutions Architect -- Professional (SAP-C02) Exam Guide: cost allocation, tagging strategies, and chargeback/showback design for shared platforms
