Amazon SAA-C03 Exam Questions

Amazon EC2 On-Demand Instances: Since the batch jobs cannot be disrupted, On-Demand Instances provide the necessary reliability and availability.

Amazon S3 Standard: Storing data in S3 Standard for the first 30 days ensures quick and frequent access.

S3 Glacier Deep Archive: After 30 days, moving data to S3 Glacier Deep Archive significantly reduces storage costs for data that is rarely accessed.

S3 Lifecycle Configuration: Automating the transition and deletion of objects using lifecycle policies ensures cost optimization and compliance with data retention requirements.

References:

Amazon S3 Storage Classes

Managing your storage lifecycleAWS Documentation

Amazon Elastic File System (EFS) is a managed file storage service that can be mounted across multiple EC2 instances. It provides a scalable and high-performing solution to share data among instances within a VPC.

High Performance: EFS provides scalable performance for workloads that require high throughput and IOPS. It is particularly well-suited for applications that need to share data across multiple instances.

Ease of Use: EFS can be easily mounted on multiple instances across different Availability Zones, providing a shared file system accessible to all the instances within the VPC.

Security: EFS can be configured to ensure that data remains within the VPC, and it supports encryption at rest and in transit.

Why Not Other Options?:

Option A (Amazon S3 bucket with APIs): While S3 is excellent for object storage, it is not a file system and does not provide the low-latency access required for shared data between instances.

Option B (S3 bucket as a mounted volume): S3 is not designed to be mounted as a file system, and this approach would introduce unnecessary complexity and latency.

Option C (EBS volume shared across instances): EBS volumes cannot be attached to multiple instances simultaneously. It is not designed to be shared across instances like EFS.

AWS References:

Amazon EFS- Overview of Amazon EFS and its features.

Best Practices for Amazon EFS- Recommendations for using EFS with multiple instances.

Amazon RDS does not support enabling encryption at rest on an existing unencrypted DB instance. To encrypt an existing RDS instance's data at rest, the recommended method is to:

Take a snapshot of the unencrypted DB instance.

Create an encrypted copy of the snapshot using AWS KMS. This encrypted snapshot contains the existing data encrypted at rest.

Restore a new DB instance from the encrypted snapshot. This new instance will have encryption at rest enabled.

Additionally, to manage encryption keys securely, companies can use customer managed keys (CMKs) in AWS Key Management Service (KMS). CMKs provide greater control over key management policies, rotation, and usage permissions compared to default AWS managed keys. Using a CMK allows customization of access control and auditability.

Option A is incorrect because you cannot enable encryption directly on a snapshot; you must create an encrypted copy. Option C is invalid because encryption cannot be enabled by modifying an existing instance's configuration. Option D refers to the default AWS managed key, which is less flexible than customer managed keys.

References:

Encrypting Amazon RDS Resources (https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Overview.Encryption.html)

Copying an Encrypted Snapshot (https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_CopySnapshot.html)

AWS KMS Customer Master Keys (https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html)

AWS Well-Architected Framework --- Security Pillar (https://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf)

Compute Savings Plans apply to EC2, AWS Fargate, and AWS Lambda usage, maximizing coverage for mixed architectures while retaining flexibility across instance families, regions, OS, and tenancy. For private connectivity, Lambda functions must be configured with VPC access to attach ENIs in the target subnets, enabling direct network access to EC2 in private subnets (no public subnets are required for intra-VPC communication). EC2 Instance Savings Plans only discount EC2 usage and are tied to a specific instance family in a region, reducing flexibility and leaving Lambda costs undiscounted. Keeping Lambda in the service VPC prevents direct access to private EC2 without VPC configuration. Thus, a 1-year Compute Savings Plan plus connecting Lambda to the private subnets minimizes total cost and meets connectivity needs.

References: AWS Savings Plans --- Compute Savings Plans applicability (EC2, Fargate, Lambda); AWS Lambda Developer Guide --- Configuring Lambda to access VPC resources (ENIs, subnets, security groups).

The most cost-effective general solution isDynamoDB auto scaling. AWS recommends atarget utilization of 70%for provisioned throughput with auto scaling, which lets DynamoDB adjust capacity up or down based on sustained workload patterns. That improves both read and write performance while controlling cost better than manual scaling. A GSI is only useful for specific alternate query patterns and does not solve overall throughput management by itself. DAX helps only with read-heavy caching, not writes. A custom CloudWatch-plus-Lambda scaling solution adds unnecessary operational work compared to native auto scaling. Because the question asks broadly for read and write performance at the best cost, auto scaling with the recommended 70% target is the strongest answer.

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