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The SAA-C03 Certification Exam is an updated version of its predecessor, SAA-C02, which reflects the latest changes in AWS services and best practices. It covers a wide range of topics, including designing and deploying applications on AWS, selecting the right AWS services for specific use cases, and understanding the cost implications of AWS services.

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2026 Amazon Newest SAA-C03: Exam AWS Certified Solutions Architect - Associate Question

The Amazon SAA-C03 certification is a valuable credential and comes with certain benefits. You can use AWS Certified Solutions Architect - Associate exam certificate to inspire managers or employers. For many professionals, the Amazon SAA-C03 Certification Exam will not only validate your expertise but also gives you an edge in the job market or the corporate ladder.

Amazon SAA-C03 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Design Secure Architectures: This section of the exam measures skills of Cloud Security Engineers and Solutions Architects and covers the design of secure architectures on AWS. Learners explore secure access to AWS resources, secure workloads and applications, and appropriate data security controls. The content addresses access controls and management across multiple accounts, AWS federated access and identity services, VPC architectures with security components, network segmentation strategies, application security integration, data access and governance, encryption and key management, and compliance requirements. The material focuses on applying AWS security best practices, designing flexible authorization models, implementing role based access control strategies, securing network connections, encrypting data at rest and in transit, and implementing data backup and protection policies.
Topic 2
  • Design Resilient Architectures: This section of the exam measures skills of Infrastructure Architects and Solutions Architects and covers the design of resilient architectures that ensure business continuity. Learners study scalable and loosely coupled architectures, highly available and fault tolerant architectures, and disaster recovery strategies. The content addresses API creation and management, caching strategies, microservices design principles, event driven architectures, horizontal and vertical scaling, load balancing concepts, serverless technologies and patterns, container orchestration, AWS global infrastructure, distributed design patterns, failover strategies, and service quotas and throttling. The material focuses on designing event driven and multi tier architectures, determining scaling strategies, achieving loose coupling, implementing automation to ensure infrastructure integrity, mitigating single points of failure, and selecting appropriate disaster recovery strategies to meet business requirements.
Topic 3
  • Design High Performing Architectures: This section of the exam measures skills of Performance Engineers and Solutions Architects and covers the design of high performing architectures that meet demanding workload requirements. Learners explore high performing and scalable storage solutions, elastic compute solutions, database solutions, network architectures, and data ingestion and transformation solutions. The content addresses hybrid storage solutions, compute services with appropriate use cases, distributed computing concepts, database capacity planning and replication, caching strategies, edge networking services, network architecture design, data analytics and visualization services, data transfer services, and streaming data services. The material focuses on determining storage configurations that meet performance demands, decoupling workloads for independent scaling, selecting appropriate compute and database options, creating network topologies for various architectures, building and securing data lakes, designing data streaming architectures, and implementing visualization strategies.
Topic 4
  • Design Cost Optimized Architectures: This section of the exam measures skills of Cloud Financial Analysts and Solutions Architects and covers the design of cost optimized architectures that maximize value while minimizing expenses. Learners study cost optimized storage solutions, compute solutions, database solutions, and network architectures. The content addresses AWS cost management service features and tools, storage access patterns and tiering, backup strategies, AWS purchasing options, distributed compute strategies, instance types and sizes, compute utilization optimization, scaling strategies, caching strategies, data retention policies, database capacity planning, load balancing concepts, NAT gateways, and network routing and peering. The material focuses on designing appropriate storage strategies, managing object lifecycles, determining cost effective compute and database services, selecting appropriate instance families and sizes, configuring appropriate network connections and routes, minimizing network transfer costs, and reviewing existing workloads for optimization opportunities.

Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q825-Q830):

NEW QUESTION # 825
A company has a regional subscription-based streaming service that runs in a single AWS Region. The architecture consists of web servers and application servers on Amazon EC2 instances. The EC2 instances are in Auto Scaling groups behind Elastic Load Balancers. The architecture includes an Amazon Aurora database cluster that extends across multiple Availability Zones.
The company wants to expand globally and to ensure that its application has minimal downtime.

Answer: B

Explanation:
This option is the most efficient because it deploys the web tier and the application tier to a second Region, which provides high availability and redundancy for the application. It also uses an Amazon Aurora global database, which is a feature that allows a single Aurora database to span multiple AWS Regions1. It also deploys the database in the primary Region and the second Region, which provides low latency global reads and fast recovery from a Regional outage. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which provides data protection by routing traffic to healthy endpoints in different Regions2. It also promotes the secondary to primary as needed, which provides data consistency by allowing write operations in one of the Regions at a time3. This solution meets the requirement of expanding globally and ensuring that its application has minimal downtime. Option A is less efficient because it extends the Auto Scaling groups for the web tier and the application tier to deploy instances in Availability Zones in a second Region, which could incur higher costs and complexity than deploying them separately. It also uses an Aurora global database to deploy the database in the primary Region and the second Region, which is correct.
However, it does not use Amazon Route 53 health checks with a failover routing policy to the second Region, which could result in traffic being routed to unhealthy endpoints. Option B is less efficient because it deploys the web tier and the application tier to a second Region, which is correct. It also adds an Aurora PostgreSQL cross-Region Aurora Replica in the second Region, which provides read scalability across Regions. However, it does not use an Aurora global database, which provides faster replication and recovery than cross-Region replicas. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which is correct. However, it does not promote the secondary to primary as needed, which could result in data inconsistency or loss. Option C is less efficient because it deploys the web tier and the application tier to a second Region, which is correct. It also creates an Aurora PostgreSQL database in the second Region, which provides data redundancy across Regions. However, it does not use an Aurora global database or cross-Region replicas, which provide faster replication and recovery than creating separate databases. It also uses AWS Database Migration Service (AWS DMS) to replicate the primary database to the second Region, which provides data migration between different sources and targets. However, it does not use an Aurora global database or cross-Region replicas, which provide faster replication and recovery than using AWS DMS. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which is correct.


NEW QUESTION # 826
A company wants to deploy an AWS Lambda function that will read and write objects to Amazon S3 bucket. The Lambda function must be connected to the company's VPC. The company must deploy the Lambda function only to private subnets in the VPC. The Lambda function must not be allowed to access the internet.
Which solutions will meet these requirements? (Select TWO.)

Answer: A,E


NEW QUESTION # 827
A company runs its infrastructure on AWS and has a registered base of 700,000 users for its document management application. The company intends to create a product that converts large .pdf files to .jpg image files. The .pdf files average 5 MB in size. The company needs to store the original files and the converted files. A solutions architect must design a scalable solution to accommodate demand that will grow rapidly over time. Which solution meets these requirements MOST cost-effectively?

Answer: B

Explanation:
Elastic BeanStalk is expensive, and DocumentDB has a 400KB max to upload files. So Lambda and S3 should be the one.


NEW QUESTION # 828
[Design High-Performing Architectures]
A company needs to extract the names of ingredients from recipe records that are stored as text files in an Amazon S3 bucket A web application will use the ingredient names to query an Amazon DynamoDB table and determine a nutrition score.
The application can handle non-food records and errors The company does not have any employees who have machine learning knowledge to develop this solution Which solution will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
This solution meets the following requirements:
It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.
It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.
It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.
It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.
Reference:
Using AWS Lambda with Amazon S3 - AWS Lambda
What Is Amazon Comprehend? - Amazon Comprehend
Working with Tables - Amazon DynamoDB


NEW QUESTION # 829
[Design Secure Architectures]
A company runs container applications by using Amazon Elastic Kubernetes Service (Amazon EKS). The company's workload is not consistent throughout the day The company wants Amazon EKS to scale in and out according to the workload.
Which combination of steps will meet these requirements with the LEAST operational overhead? {Select TWO.)

Answer: A,C

Explanation:
https://docs.aws.amazon.com/eks/latest/userguide/horizontal-pod-autoscaler.html
https://docs.aws.amazon.com/eks/latest/userguide/autoscaling.html
Horizontal pod autoscaling is a feature of Kubernetes that automatically scales the number of pods in a deployment, replication controller, or replica set based on that resource's CPU utilization. It requires ametrics source such as the Kubernetes Metrics Server to provide CPU usage data1. Cluster autoscaling is a feature of Kubernetes that automatically adjusts the number of nodes in a cluster when pods fail or are rescheduled onto other nodes. It requires an integration with AWS Auto Scaling groups to manage the EC2 instances that join the cluster2. By using both horizontal pod autoscaling and cluster autoscaling, the solution can ensure that Amazon EKS scales in and out according to the workload.


NEW QUESTION # 830
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