SAA-C03 Premium Exam Questions

Amazon EFS requires a mount target in each Availability Zone where EC2 instances access the file system. This is because each mount target provides an elastic network interface in the subnet and AZ, reducing network latency by allowing EC2 instances to communicate locally with the EFS mount target. Creating a mount target in each AZ optimizes file system access performance and availability. Instances mount the EFS file system via the mount target in their respective AZ, which provides the lowest possible latency and avoids cross-AZ traffic.

Option A, with only a single mount target in the VPC, will cause cross-AZ traffic for instances in other AZs, increasing latency and potentially incurring data transfer costs. Option B is incomplete and introduces complexity with sharing directories across instances. Option C is invalid because mount targets are per AZ and per subnet, not per instance.

Amazon S3 File Gateway (AWS Storage Gateway) exposes an on-premises NFS/SMB file share that durably stores files as S3 objects with local caching, enabling low-latency writes on-premises and asynchronous, near-real-time ingestion into Amazon S3 for analytics. It is purpose-built to “present a file interface backed by Amazon S3” and to “store files as objects in your S3 buckets,” so existing applications writing to a network share can transparently land data in S3 without custom scripts or job orchestration. Compared with scheduled transfers (e.g., end-of-day DataSync), S3 File Gateway continuously uploads new/changed files, better meeting the near-real-time requirement. Transfer Family (SFTP) would require custom polling and client changes, increasing operational burden. DataSync is excellent for bulk or periodic migrations/synchronizations but is not as seamless for continuous ingestion from a live file share. Therefore, updating the application to point to an S3 File Gateway share provides the simplest, performant path to near-real-time delivery into S3 for downstream analytics.

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

The requirements emphasize high availability across multiple Availability Zones, automatic recovery, and least operational overhead. The simplest way to achieve this for containerized microservices is to use a fully managed container orchestration service with serverless compute. Amazon ECS with the Fargate launch type provides exactly that.

With ECS Fargate, AWS manages the underlying compute infrastructure (no EC2 instances to provision, patch, scale, or secure at the OS level). You define task definitions and services, and ECS schedules tasks across Availability Zones based on your networking configuration (multiple subnets in different AZs). If a container fails, ECS service scheduling automatically replaces it, fulfilling the “recover from failures” requirement. Fargate also integrates with load balancing, CloudWatch logging, and scaling policies, reducing the operational burden of keeping the platform healthy.

Option A (ECS on EC2) is still managed orchestration, but it requires managing the EC2 capacity layer: AMI updates, instance patching, cluster capacity planning, and instance scaling. Option B (EKS with self-managed nodes) typically carries even more operational responsibility because you manage node groups, upgrades, and Kubernetes operational complexity in addition to the underlying instances. Option D is the highest operational overhead because it lacks managed scheduling and self-healing; you would need to build and maintain your own process manager and failover mechanisms.

Therefore, C is the best answer because it provides a managed, multi-AZ capable, self-healing container platform with minimal infrastructure management—matching the operational efficiency goal while maintaining high availability.