Amazon Web Services Updated SAA-C03 Exam Questions and Answers by sufyan

To access an EFS file system across accounts and VPCs, the EFS must be mounted using VPC peering or AWS Transit Gateway, and the EC2 instances must use the amazon-efs-utils package with the correct mount target or access point.

Using an EFS access point simplifies access management, especially across accounts, by providing a POSIX identity and access policy layer.

VPC sharing doesn’t support EFS directly unless the subnet and resources are shared properly, which requires redeployment. Therefore, option D is the most complete and correct.

Amazon Aurora MySQL–compatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirements “shard-level control” and “custom checkpointing” point directly to the Kinesis Client Library (KCL), which is designed for building consumer applications that coordinate shard processing, perform load balancing across workers, and manage checkpoints in a durable store (commonly DynamoDB) with fine-grained control. While Lambda can consume from Kinesis Data Streams, its event source mapping abstracts shard coordination and checkpoint behavior; it is not the best fit when you explicitly need custom checkpointing logic and shard-level control beyond the managed integration.

Running the existing Lambda processing logic as a long-running consumer in Amazon ECS on AWS Fargate allows the company to operate a scalable KCL-based consumer fleet without managing servers. With Fargate, AWS manages the underlying compute while the application maintains direct control over shard assignment and checkpoint timing/frequency through KCL—exactly what the requirement calls for. Latency is minimized because the consumer reads directly from Kinesis Data Streams and processes records continuously, avoiding additional buffering layers or store-and-forward patterns.

Option A adds significant latency by delivering to S3 through Firehose and then triggering processing from S3 object notifications; this is optimized for delivery and batch-style processing, not low-latency streaming with shard-level control. Options B and D introduce SQS between Kinesis and processing, which adds another hop and does not inherently provide shard-level control or KCL-style checkpointing; FIFO also limits throughput and is not intended for high-scale streaming fan-in. Increasing Lambda provisioned concurrency can reduce cold starts, but it does not solve the need for custom checkpointing at the shard level.

Therefore, C best meets shard-level control and custom checkpointing requirements with the least latency by using a direct KCL consumer on a managed compute platform (Fargate).

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.