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

The correct answer is D.

D. This solution meets the requirements because it uses a scheduled scaling policy for the EC2 instances, which can adjust the capacity according to the known sale events. It also uses Amazon Aurora PostgreSQL Multi-AZ DB cluster, which provides high availability and durability for the database. It uses Amazon EventBridge rules and AWS Lambda functions to create a larger Aurora Replica before a sale event and fail over to it, which can improve the performance and handle the increased traffic.It also uses anotherEventBridge rule and Lambda function to scale down the Aurora Replica after the sale event, which can save costs123

A. This solution is incorrect because it uses predictive scaling policy for the EC2 instances, which is not suitable for one-time events that are scheduled. Predictive scaling is based on historical data and machine learning, which may not accurately forecast the demand for sale events. It also uses Amazon Aurora PostgreSQL Serverless v2 Multi-AZ DB instance, which does not support read replicas.The use of AWS Step Functions state machine and Lambda functions to pre-warm the database is unnecessary and adds complexity45

B. This solution is incorrect because it uses Amazon RDS for PostgreSQL Multi-AZ DB instance with automatically scaling read replicas, which may not provide enough performance improvement for the sale events. The use of EventBridge rules and Lambda functions to create a larger read replica and fail over to it is risky and may cause downtime ordata loss.The use of another EventBridge rule and Lambda function to scale down the read replica is also risky and may cause inconsistency or data loss67

C. This solution is incorrect because it uses predictive scaling policy for the EC2 instances, which is not suitable for one-time events that are scheduled. Predictive scaling is based on historical data and machine learning, which may not accurately forecast the demand for sale events.The use of AWS Step Functions state machine and Lambda functions to pre-warm the database is unnecessary and adds complexity45

The company needs to upgrade DocumentDB to the latest version with no data loss while allowing continuous reads and writes. The company also must be able to test and verify the upgrade before switching production traffic. This is a classic requirement for performing an upgrade using a blue/green approach: build a new target environment on the new version, keep it in sync with the source, validate it, and then cut over by changing the endpoint (here, Route 53 DNS).

Option A implements this pattern using a new DocumentDB cluster running the latest version and AWS DMS to continuously migrate and replicate changes from the old cluster to the new cluster. Because the workload is continuously changing, a one-time export/import is insufficient; continuous replication is needed to keep the target cluster current during the test period. AWS DMS supports a “migrate and replicate” style of task that performs a full load and then applies ongoing changes (CDC) so the target stays synchronized. The question also states that change streams are enabled with a 24-hour retention period, which supports capturing and applying changes during migration/validation and helps ensure the replication stream can be maintained while testing.

Option A also addresses indexes by using the DocumentDB Index Tool to export and import indexes, which is important because indexes can affect query performance and behavior. After the company validates the new cluster, the cutover is done by updating the Route 53 record to point to the new cluster endpoint, switching all backend services without changing application configuration beyond DNS resolution.

Option B uses MongoDB CLI tools to export/import. This is not suitable for continuous write workloads because export/import is a point-in-time operation and would require downtime or risk data divergence during the test period. It also adds more operational overhead and does not provide continuous replication for the duration of validation.

Option C performs an in-place major version upgrade. That does not satisfy the requirement to test and verify the upgrade before backend services use the upgraded version because the upgrade happens directly on the production cluster. Even though a snapshot exists for rollback, production is still exposed to the upgrade immediately, which violates the requirement for pre-cutover verification.

Option D is incorrect because AWS DataSync transfers files between storage systems such as NFS/SMB and AWS storage services. It is not a database migration or replication service and cannot copy a DocumentDB database in a way that preserves database semantics and supports continuous replication.

Therefore, creating a new DocumentDB cluster, keeping it synchronized using AWS DMS (supported by change stream retention), validating it, and then cutting over via Route 53 DNS update (option A) meets all requirements.

it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.