Amazon Web Services SAP-C02 Questions Answers

Comprehensive and Detailed Explanation:

This question is mainly about cost optimization for two different workload patterns:

A constant-rate ingestion workload

A noncritical nightly batch analytics workload that can tolerate interruption or rerun

The most cost-effective architecture is to decouple storage from the ingestion servers, store the incoming data in Amazon S3, and run the nightly calculations by using AWS Batch on Spot Instances.

Why B is correct

Amazon Data Firehose can continuously ingest streaming data and deliver the data to Amazon S3. Because the data rate is constant, the company does not need to keep a fleet of EC2 instances running just to collect and retain data on attached EBS volumes. Using S3 as durable storage is operationally simpler and generally more cost-effective than maintaining long-running EC2 ingestion servers with EBS-based storage for this type of streaming landing zone.

For the nightly processing, the job is:

batch oriented,

takes only 4 hours,

not critical to the business,

acceptable to rerun later if interrupted.

These characteristics make Spot Instances an ideal fit. AWS Batch is specifically designed to run batch computing workloads and can efficiently provision and manage compute resources, including Spot Instances. Since the job can tolerate interruption, Spot capacity provides the lowest-cost compute option.

This option reduces cost in both major areas:

storage and ingestion are moved to managed services and object storage,

nightly compute moves from On-Demand Instances to Spot-based batch processing.

Why A is incorrect

A improves the design by moving ingestion to Data Firehose and storage to S3, which is a good step. However, it still uses EC2 On-Demand Instances for nightly processing. Because the nightly workload is not business-critical and can tolerate failure, Spot Instances are a better fit and more cost-effective than On-Demand Instances. Therefore, A is not the most cost-effective choice.

Why C is incorrect

This option keeps ingestion on a fleet of EC2 Reserved Instances with 3-year reservations behind a Network Load Balancer. That is not the most economical or simplest design for constant ingestion when a managed ingestion service such as Data Firehose can deliver directly to S3.

Also, a Network Load Balancer does not add meaningful value for the ingestion requirement described here. The issue is not distributing interactive network traffic to an application fleet. The better architecture is to use a managed data ingestion service instead of recommitting to long-term EC2 reservations.

While AWS Batch with Spot Instances is good for the nightly analytics portion, the ingestion side of this option is still more expensive and less efficient than B.

Why D is incorrect

Amazon Redshift is a data warehouse and is useful for analytical querying, but this option is not the most cost-effective for the stated use case. The company needs to ingest constant-rate streaming market data and run a nightly aggregate job. Storing all incoming data in Redshift is generally a more specialized and potentially more expensive architecture than landing the raw stream in S3 and running elastic batch processing on demand.

The Lambda part is also problematic. The batch processing currently takes 4 hours. AWS Lambda is not designed for long-running heavy batch jobs of this kind. Even if the aggregate query could be expressed in SQL, the answer is still not the most cost-effective and does not align as well with the workload pattern as S3 plus AWS Batch on Spot Instances.

Key design principles being tested

This question tests whether you can identify:

S3 as low-cost, durable storage for incoming streamed data

Managed ingestion services instead of self-managed EC2 ingestion servers

Spot Instances for interruptible, fault-tolerant batch jobs

AWS Batch as the managed orchestration service for batch workloads

Final justification

The most cost-effective redesign is to:

ingest the streaming data with Amazon Data Firehose,

store the data in Amazon S3,

run nightly analytics through AWS Batch on Spot Instances.

That makes B the best answer.

This solution will meet the requirements of making the application highly available with the least development time. Creating a new AMI that is configured with SSM Agent will enable the company to use AWS Systems Manager to manage and patch the EC2 instances automatically, reducing downtime and human errors. Using a launch template for an Auto Scaling group will allow the company to launch multiple instances of the same configuration and scale them up or down based on demand. Using smaller instances in the Auto Scaling group will reduce the cost and improve the performance of the application tier. Creating an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group will increase the availability and fault tolerance of the application tier. Migrating the database to Amazon Aurora MySQL will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections.

The company needs the application in the secondary Region to operate independently from the primary Region during disaster recovery. That means both the DynamoDB data and the relational customer data must be present and usable in the secondary Region without cross-Region dependencies at runtime. The company also wants the least development effort, which usually means using native managed replication features rather than building custom replication logic.

For DynamoDB, global tables are the managed multi-Region, multi-active replication feature. Global tables replicate changes between Regions automatically and allow applications in each Region to read and write to local DynamoDB tables, removing dependencies on the primary Region.

For the RDS for MySQL database, the straightforward managed approach for cross-Region disaster recovery with minimal development change is to create a cross-Region read replica in the secondary Region. A read replica uses asynchronous replication to keep data up to date. During a disaster recovery event, the company can promote the read replica in the secondary Region to become a standalone primary database instance. This provides a simple, low-development approach for having the data in the secondary Region. The application in the secondary Region can be configured to use the replica endpoint in normal times and can continue operating with the promoted instance during failover.

Option A combines DynamoDB global tables with an RDS read replica in the secondary Region. This is the least development effort because it uses AWS-managed replication for both data stores and requires only configuration changes and endpoint selection in the secondary application.

Option B is not correct because DAX is a caching layer for DynamoDB, not a replication or disaster recovery mechanism. DAX does not replicate DynamoDB tables into another Region and cannot replace having the DynamoDB data present in the secondary Region.

Option C is incorrect because RDS Multi-AZ is a high availability feature within a single Region, using synchronous replication to a standby in another Availability Zone. Multi-AZ does not create a standby in a different Region, and it does not provide cross-Region DR by itself.

Option D is less desirable because it requires custom development and operations: processing DynamoDB streams, building and operating a replication pipeline, and ensuring correctness and ordering across Regions. This is higher development effort than enabling DynamoDB global tables.

Therefore, using DynamoDB global tables plus an RDS read replica in the secondary Region (option A) meets the DR independence requirement with the least development effort.