Amazon Web Services SAP-C02 Exam With Confidence Using Practice Dumps

For a development environment that requires frequent resource recreation and connectivity to applications hosted in a shared services account, the most efficient solution involves using AWS Resource Access Manager (RAM) and the transit gateway in the shared services account. By turning on automatic acceptance for the transit gateway in the shared services account and sharing it with the development account through AWS RAM, the development team can easily recreate their connection as needed without manual intervention. This setup allows for scalable, flexible connectivity between accounts while minimizing operational overhead and ensuring consistent access to shared services.

AWS Documentation on AWS Resource Access Manager and Transit Gateway provides guidance on sharing network resources across AWS accounts and enabling automatic acceptance for transit gateway attachments. This approach is also supported by AWS best practices for multi-account strategies using AWS Organizations and network architecture.

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.

The compute layer uses AWS Fargate, so the correct savings instrument is a Compute Savings Plan, not an EC2 Instance Savings Plan. AWS states that Compute Savings Plans automatically apply to EC2, Fargate, and Lambda usage, while EC2 Instance Savings Plans are restricted to EC2 instance usage in a selected family and Region. The database layer is Aurora MySQL, so a Reserved DB Instance is appropriate only for the predictable production database, not for development and testing databases that are regularly shut down. A 1-year commitment also matches the stated one-year predictable production demand. Buying 3-year commitments for all accounts would overcommit spend, especially for non-production environments with intermittent usage. Therefore, centralized 1-year production database reservations plus a 1-year Compute Savings Plan is the most cost-effective match.