AWS Certified Solutions Architect Associate Practice Test (SAA-C03)

Configuring an Auto Scaling group with dynamic scaling policies based on target tracking allows the application to automatically adjust the number of EC2 instances in response to real-time demand. This strategy ensures sufficient capacity during traffic spikes and reduces unnecessary costs during low traffic periods by terminating unneeded instances. Manually adjusting the instance count is not practical for unpredictable traffic and requires ongoing management. Using larger instance types increases capacity is referred as vertical scaling and it may not handle extreme unpredictable spikes. Maintaining maximum capacity at all times wastes resources during periods of low demand.

Given the requirements of handling asynchronous and unpredictable bursts of data without a need for a constantly running server, a serverless approach is most cost-effective. The recommended service, AWS Lambda, allows the startup to run code in response to triggers and automatically manages the scaling. There is no charge for when the code isn't running, matching the sporadic workload, thereby reducing operational costs. Other services like EC2, even with Spot Instances, wouldn't be cost-effective due to the potential of paying for idle time. Similarly, EC2 Reserved Instances would not fit the unpredictable pattern and would involve a long-term commitment. AWS Fargate offers task-based pricing but still incurs costs for the duration the task is running, which might not be as cost-effective for very short-lived tasks.

Using the selected service, enterprises can manage multiple environments by setting up a well-architected baseline, automating the provisioning of new environments, and uniformly applying policy controls across all environments for security and compliance. While the other options provide specific security features or advisory services, they do not offer the comprehensive solution needed for centralized governance and automated environment setup.

Amazon EMR, a service that enables customers to run big data frameworks such as Hadoop, Spark, and HBase in a managed cluster environment is the correct choice for processing extensive datasets that include batch jobs and interactive analytics. This service allows customers to focus on analytics without the operational burden of managing infrastructure. Other options may be used for computing but they do not offer the managed environment for big data and analytics frameworks, thereby increasing the operational complexity for the given use case.

Organizing users into groups based on their access requirements and attaching appropriate policies to these groups is the most effective approach. This method implements a role based access control (RBAC) which allows for centralized permission management, making it easier to assign or revoke access as team members join or leave. It also reduces the complexity of managing individual permissions for each user and minimizes the risk of granting unnecessary privileges. Attaching permissions directly to individual user accounts is not scalable as the team grows. Using roles for each permission set is more suitable for temporary or cross-account access rather than managing team permissions. Granting all users full access goes against the principle of least privilege and poses security risks.

The most secure way to provide the necessary permissions is by creating a role in Account B with the appropriate permissions to the object storage and setting up a trust relationship that allows the execution role of the function in Account A to assume this role when needed. This method upholds the practice of granting least privilege access. The other methods either do not provide the direct cross-account functionality required, may lead to unnecessary exposure of credentials, or do not properly adhere to the principle of least privilege.

Using a fully managed database proxy service allows ephemeral compute instances, like AWS Lambda functions, to efficiently manage database connections. This service can absorb the variability in concurrent connections, smoothing out spikes in traffic and preserving backend database stability. It achieves this through connection pooling and multiplexing, which enhances application scalability and resilience without exhausting connections. While other options may improve availability (Multi-AZ) or read performance (Read Replica), they do not offer the specific benefits of a managed proxy service when it comes to connection pooling. Scaling the compute engine vertically would not directly solve the problem of efficiently managing a large number of connections and could lead to resource exhaustion under high traffic conditions.

AWS Lambda is most suitable for the described scenario as it provides a serverless environment that can handle sporadic, concurrent requests without the need for server management. Lambda allows the system to scale automatically and precisely with the incoming workload, ensuring that costs directly correlate to usage. While AWS Batch efficiently processes batch jobs, its predetermined scaling may not align perfectly with unpredictable message arrival times, leading to possible delays or over-provisioning. Amazon EC2 Spot Instances are cost-effective but require management for in-depth capacity and scaling, which does not match the management's preference. Amazon ECS on AWS Fargate could meet the needs but will incur more costs compared to Lambda because Fargate’s pricing is based on container runtime and resources regardless of message arrivals.

By leveraging Amazon Simple Queue Service (Amazon SQS), the application can enqueue incoming orders, allowing the processing component to consume messages at a regulated pace. This introduces a layer that decouples the order intake from processing, thereby enhancing scalability as the message queue can absorb sudden bursts of traffic. Immediate execution through synchronous invocation leads to tight coupling and possible system overload during peak times. Other solutions, like streams or database triggers, do not offer the same level of decoupling and might introduce complexity that is unnecessary for this use case.

Selecting Amazon RDS with Reserved Instances provides cost savings over on-demand instance pricing due to the commitment to a specific usage term. This approach is beneficial for databases with predictable performance needs, such as the one described, as it allows the company to plan its capacity and manage costs effectively. Using the on-demand pricing model would be less cost-efficient given the predictable, consistent demand. Although Aurora and DynamoDB provide performance benefits, they might not always translate to cost savings in cases where the workload is stable and the capacity is predictable, making them less optimal for this scenario. Spot instances are not suitable for mission-critical databases due to the possibility of interruption.

For predictable baseline usage, Reserved Instances provide a significant discount compared to On-Demand pricing in exchange for a commitment to a consistent amount of compute capacity. To handle the variable peak loads during weekends, On-Demand Instances or Spot Instances can be used. Given the need for availability during peak times, relying solely on Spot Instances which can be interrupted is risky; therefore, a mix of Reserved Instances for the baseline and On-Demand Instances to handle peaks can be the most cost-effective and reliable approach as it provides cost savings for the predictable demand and flexibility to scale with reliable instances during spikes.

According to the shared responsibility model for security in the cloud, while the provider is responsible for the security of the cloud infrastructure (computing hardware, storage, and networking), the customer is responsible for securing the data processed and stored within the cloud environment. This extends to client-side data encryption, which is the customer's duty, and not the provider's.

Amazon DynamoDB is the optimal solution for this use case as it provides a NoSQL database with the ability to scale automatically to accommodate high ingest rates of transaction records. It is designed for applications that require consistent, single-digit millisecond latency for any scale. Additionally, DynamoDB offers strong consistency, ensuring that after a write, any subsequent read will reflect the change. In contrast, RDS is better suited for structured data requiring relational capabilities, Neptune is tailored for graph database use cases, and DocumentDB is optimized for JSON document storage which, while capable of handling key-value pairs, is not as cost-effective or performant for this specific scenario as DynamoDB.

Leveraging an external session store, such as DynamoDB or ElastiCache, to manage the session state outside the microservice allows for statelessness within the microservice itself. This approach enables the microservice to remain stateless while persisting user session data, thereby maintaining scalability and resilient failover capabilities. Implementing a stateful microservice that retains user sessions would hinder scalability and complicate failover processes. Storing session states in a relational database like RDS could introduce unnecessary complexity and potential single points of failure, and relying solely on in-memory storage would risk data loss in the event of a service disruption or restart, thus failing to persist the state between sessions.

Shipping the data using a physical data transport appliance like Snowball Edge provides the most cost-effective and time-efficient method for transferring large amounts of data to the cloud. It avoids the limitations of network bandwidth and reduces costs associated with prolonged Internet transfers. Transferring 50 TB over a 500 Mbps connection would take over 9 days, exceeding the one-week requirement, and could incur higher costs due to extended network usage. Using data transfer services over the Internet or uploading via command-line tools would face similar time constraints and potentially higher costs. Establishing a dedicated network connection is not feasible within a week and involves substantial setup costs.