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

Enable DynamoDB Auto Scaling:

Navigate to the DynamoDB console and select the table experiencing high demand.

Go to the " Capacity " tab and enable auto scaling for both read and write capacity units. Auto scaling adjusts the provisioned throughput capacity automatically in response to actual traffic patterns, ensuring the table can handle the increased load.

Configure Auto Scaling Policies:

Set the minimum and maximum capacity units to define the range within which auto scaling can adjust the provisioned throughput.

Specify target utilization percentages for read and write operations, typically around 70%, to maintain a balance between performance and cost.

Monitor and Adjust:

Use Amazon CloudWatch to monitor the auto scaling activity and ensure it is effectively handling the increased demand.

Adjust the auto scaling settings if necessary to better match the traffic patterns and application requirements.

By enabling DynamoDB auto scaling, you ensure that the database can handle the fluctuating traffic volumes without manual intervention, improving response times and reducing errors.

References

AWS Compute Blog on Using API Gateway as a Proxy for DynamoDB【60】.

AWS Database Blog on DynamoDB Accelerator (DAX)【59】.

The current architecture uses a fleet of small EC2 instances that poll SQS for URLs and write results to EFS. Because URLs arrive only occasionally and each crawl takes 10 seconds or less, EC2 instances are often idle waiting for messages. This leads to unnecessary compute and storage costs.

This workload is naturally event-driven: each URL in the SQS queue triggers a short-lived crawl operation. AWS Lambda is well suited for such event-driven, short-duration tasks. Lambda integrates natively with Amazon SQS as an event source, which allows Lambda to poll the SQS queue automatically and invoke functions when messages arrive, scaling the concurrency up and down as needed. When there are no messages, there are no Lambda invocations and no compute charges, eliminating the cost of idle EC2 instances.

For storage of crawl results, Amazon S3 is a highly durable, cost-effective object store. The current use of an EFS file system mounted on all instances is no longer necessary if each Lambda invocation can write the output directly to S3 as objects (for example, CSV files). S3 charges only for the storage used and requests, and does not require continuously running file system infrastructure.

Option B replaces the idle fleet of EC2 instances with a Lambda function that reads from SQS. This aligns compute usage with actual work and takes advantage of serverless scaling and pricing.

Option E modifies the web-crawling process to store results in Amazon S3 instead of EFS, removing the need to maintain an EFS file system and its associated costs.

Option A increases instance size to m5.8xlarge, which greatly increases capacity and cost. Reducing the number of instances by 50% does not offset the large increase in instance size; it does not address idle capacity and likely increases overall cost.

Option C uses Amazon Neptune, which is a managed graph database service. It is not needed for storing simple CSV output; it is more complex and more expensive than S3 for this use case.

Option D uses Aurora Serverless MySQL. While Aurora Serverless can automatically scale, using a relational database to store simple crawl outputs adds unnecessary cost and operational complexity compared to S3 objects.

Therefore, moving the crawling logic to Lambda triggered by SQS (option B) and writing results directly to Amazon S3 (option E) meets the requirements in the most cost-effective way.

The requirements emphasize minimum downtime, rapid migration, managed database services, high availability, and support for stored procedures. For Oracle-to-AWS migrations with minimal downtime, AWS DMS can replicate ongoing changes from the source database to the target, which reduces cutover downtime. However, a critical constraint is that the database contains stored procedures. If the company moves to a different engine, it must convert schema objects and procedural code.

Option D is the only option that combines an online replication approach (AWS DMS) with a managed, highly available target database (Amazon Aurora PostgreSQL) and explicitly includes schema and stored procedure conversion with AWS SCT. This aligns with a fast migration approach: SCT prepares the converted schema and stored procedures, while DMS performs ongoing replication of data changes until cutover, minimizing downtime. Aurora PostgreSQL is a managed service and supports high availability through multi-AZ architecture and managed failover capabilities within a Region.

Option A is incorrect because Amazon RDS does not use an S3 bucket “as database storage” in the way described. RDS storage is managed by the RDS service, and S3 replication is not the mechanism used to provide database high availability. High availability for RDS is typically achieved through Multi-AZ configurations and/or cluster-based architectures depending on engine. The described storage approach is not a valid managed RDS architecture.

Option B does not meet the managed database requirement because it restores Oracle onto EC2 instances, which is self-managed. It also does not meet minimum downtime because it requires shutting down the on-premises database to avoid new transactions before restoring the backup. This is a longer outage model than an online migration with continuous replication.

Option C is not feasible and would require major redesign. DynamoDB is not a relational database replacement for Oracle stored procedures. DAX is a DynamoDB cache and cannot run stored procedures. The option also incorrectly suggests running stored procedures “in DAX.” This does not meet the “migrate as quickly as possible with minimum downtime” requirement due to extensive refactoring and redesign.

Therefore, migrating to Aurora PostgreSQL using AWS SCT for schema and stored procedure conversion, and AWS DMS for ongoing replication to minimize downtime, is the best fit.