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Amazon SAA-C03 exam is suitable for IT professionals with at least one year of experience in designing and deploying AWS-based solutions. Candidates are expected to have a good understanding of AWS services and features, as well as experience designing and implementing solutions that leverage AWS services. SAA-C03 Exam is available in multiple languages and can be taken online or in-person at a testing center.
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Amazon AWS Certified Solutions Architect - Associate (SAA-C03) exam is a certification that validates an individual's expertise in designing and deploying scalable, highly available, and fault-tolerant systems on the Amazon Web Services (AWS) platform. AWS Certified Solutions Architect - Associate certification is intended for professionals who have a solid understanding of AWS services and can design and implement solutions that meet business requirements. The SAA-C03 Exam measures the knowledge and skills necessary to design and deploy AWS services that are secure, cost-effective, and highly available.
Earning the Amazon AWS Certified Solutions Architect - Associate certification can open up new career opportunities and increase your earning potential. AWS Certified Solutions Architect - Associate certification demonstrates to employers that you have the skills and knowledge needed to design and deploy scalable and highly available systems on the AWS platform. It also shows that you are committed to staying up-to-date with the latest cloud computing technologies and best practices.
NEW QUESTION # 412
A company's packaged application dynamically creates and returns single-use text files in response to user requests. The company is using Amazon CloudFront for distribution, but wants to further reduce data transfer costs. The company cannot modify the application's source code.
What should a solutions architect do to reduce costs?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:
Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression - which directly reduces data transfer cost.
"Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred."
- Lambda@Edge Use Cases
Since code modifications aren't allowed, using Lambda@Edge is a non-invasive way to:
Compress responses.
Reduce transfer size = lower CloudFront cost.
Incorrect Options:
B: S3 Transfer Acceleration improves speed, not cost.
C: Caching doesn't help if content is always unique (single-use).
D: Multipart uploads help with large file uploads, not transfers.
Reference:
Lambda@Edge for Content Compression
CloudFront Pricing and Cost Optimization
NEW QUESTION # 413
A company runs an application on Microsoft SQL Server databases in an on-premises data center. The company wants to migrate to AWS and optimize costs for its infrastructure on AWS.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Aurora PostgreSQL with Babelfish allows SQL Server applications to run directly on Aurora PostgreSQL with minimal code changes. Babelfish adds a SQL Server-compatible endpoint, significantly lowering costs compared to running licensed SQL Server instances on RDS or EC2.
AWS Documentation Extract:
"Babelfish for Aurora PostgreSQL enables Aurora to understand T-SQL and SQL Server wire protocol, allowing you to run SQL Server applications on Amazon Aurora PostgreSQL with lower costs." (Source: Babelfish for Aurora PostgreSQL documentation) A, D: SQL Server on EC2 or RDS incurs high Microsoft licensing costs.
C: Plain PostgreSQL would require more code refactoring than Babelfish.
Reference: AWS Certified Solutions Architect - Official Study Guide, Babelfish and Cost Optimization.
NEW QUESTION # 414
A retail company has several businesses. The IT team for each business manages its own AWS account. Each team account is part of an organization in AWS Organizations. Each team monitors its product inventory levels in an Amazon DynamoDB table in the team's own AWS account.
The company is deploying a central inventory reporting application into a shared AWS account. The application must be able to read items from all the teams' DynamoDB tables.
Which authentication option will meet these requirements MOST securely?
Answer: C
Explanation:
This solution meets the requirements most securely because it uses IAM roles and the STS AssumeRole API operation to authenticate and authorize the inventory application to access the DynamoDB tables in different accounts. IAM roles are more secure than IAM users or certificates because they do not require long-term credentials or passwords. Instead, IAM roles provide temporary security credentials that are automatically rotated and can be configured with a limited duration. The STS AssumeRole API operation enables you to request temporary credentials for a role that you are allowed to assume. By using this operation, you can delegate access to resources that are in different AWS accounts that you own or that are owned by third parties. The trust policy of the role defines which entities can assume the role, and the permissions policy of the role defines which actions can be performed on the resources. By using this solution, you can avoid hard-coding credentials or certificates in the inventory application, and you can also avoid storing them in Secrets Manager or ACM. You can also leverage the built-in security features of IAM and STS, such as MFA, access logging, and policy conditions.
Reference:
IAM Roles
STS AssumeRole
Tutorial: Delegate Access Across AWS Accounts Using IAM Roles
NEW QUESTION # 415
A company runs container applications by using Amazon Elastic Kubernetes Service (Amazon EKS) and the Kubernetes Horizontal Pod Autoscaler. The workload is not consistent throughout the day. A solutions architect notices that the number of nodes does not automatically scale out when the existing nodes have reached maximum capacity in the cluster, which causes performance issues.
Which solution will resolve this issue with the LEAST administrative overhead?
Answer: B
Explanation:
* The Kubernetes Horizontal Pod Autoscaler (HPA) scales pods, not nodes. When the cluster runs out of node capacity, the HPA cannot schedule more pods.
* On Amazon EKS, AWS recommends using the Kubernetes Cluster Autoscaler to automatically adjust the number of nodes in the cluster's underlying Auto Scaling group based on pending pods.
* Cluster Autoscaler watches for pods that cannot be scheduled because of insufficient resources and then scales out nodes automatically with minimal administrative overhead. It also scales in nodes when they are underutilized.
Why others are not correct:
* A: Just tracking memory usage does not automatically scale nodes or integrate with Kubernetes scheduling.
* C: A custom Lambda-based resizer is unnecessary undifferentiated heavy lifting compared to the native Cluster Autoscaler.
* D: An Auto Scaling group is already typically used under EKS, but by itself it does not respond to pod scheduling pressure without Cluster Autoscaler.
NEW QUESTION # 416
[Design Secure Architectures]
A company needs to optimize its Amazon S3 storage costs for an application that generates many files that cannot be recreated Each file is approximately 5 MB and is stored in Amazon S3 Standard storage.
The company must store the files for 4 years before the files can be deleted The files must be immediately accessible The files are frequently accessed in the first 30 days of object creation, but they are rarely accessed after the first 30 days.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.
Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.
Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.
Reference:
Amazon S3 Storage Classes
S3 Lifecycle Configuration
NEW QUESTION # 417
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