How can a company detect and notify security teams about PII in S3 buckets?
Amazon Macie is purpose-built for detecting PII in S3.
Option A uses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.
Options B and D involve GuardDuty, which is not designed for PII detection.
Option C uses SQS, which is less suitable for immediate notifications.
An ecommerce company is migrating its on-premises workload to the AWS Cloud. The workload currently consists of a web application and a backend Microsoft SQL database for storage.
The company expects a high volume of customers during a promotional event. The new infrastructure in the AWS Cloud must be highly available and scalable.
Which solution will meet these requirements with the LEAST administrative overhead?
To ensure high availability and scalability, the web application should run in an Auto Scaling group across two Availability Zones behind an Application Load Balancer (ALB). The database should be migrated to Amazon RDS with Multi-AZ deployment, which ensures fault tolerance and automatic failover in case of an AZ failure. This setup minimizes administrative overhead while meeting the company's requirements for high availability and scalability.
Option A: Read replicas are typically used for scaling read operations, and Multi-AZ provides better availability for a transactional database.
Option B: Replicating across AWS Regions adds unnecessary complexity for a single web application.
Option D: EC2 instances across three Availability Zones add unnecessary complexity for this scenario.
AWS Reference:
A company is developing a new application that uses a relational database to store user data and application configurations. The company expects the application to have steady user growth. The company expects the database usage to be variable and read-heavy, with occasional writes.
The company wants to cost-optimize the database solution. The company wants to use an AWS managed database solution that will provide the necessary performance.
Which solution will meet these requirements MOST cost-effectively?
Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.
Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.
Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.
Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.
Why Not Other Options?:
Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.
Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.
Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn't provide the necessary performance.
AWS Reference:
Amazon Aurora Serverless - Information on how Aurora Serverless works and its use cases.
Amazon Aurora Pricing - Details on the cost-effectiveness of Aurora Serverless.
A company has migrated several applications to AWS in the past 3 months. The company wants to know the breakdown of costs for each of these applications. The company wants to receive a regular report that Includes this Information.
Which solution will meet these requirements MOST cost-effectively?
This solution is the most cost-effective and efficient way to break down costs per application.
Tagging Resources: By tagging all AWS resources with a specific key (e.g., 'cost') and a value representing the application's name, you can easily identify and categorize costs associated with each application. This tagging strategy allows for granular tracking of costs within AWS.
Activating Cost Allocation Tags: Once tags are applied to resources, you need to activate cost allocation tags in the AWS Billing and Cost Management console. This ensures that the costs associated with each tag are included in your billing reports and can be used for cost analysis.
AWS Cost Explorer: Cost Explorer is a powerful tool that allows you to visualize, understand, and manage your AWS costs and usage over time. You can filter and group your cost data by the tags you've applied to resources, enabling you to easily see the cost breakdown for each application. Cost Explorer also supports generating regular reports, which can be scheduled and emailed to stakeholders.
Why Not Other Options?:
Option A (AWS Budgets): AWS Budgets is more focused on setting cost and usage thresholds and monitoring them, rather than providing detailed cost breakdowns by application.
Option B (Load Cost and Usage Reports into RDS): This approach is less cost-effective and involves more operational overhead, as it requires setting up and maintaining an RDS instance and running SQL queries.
Option D (AWS Billing and Cost Management Console): While you can download bills, this method is more manual and less dynamic compared to using Cost Explorer with activated tags.
AWS Reference:
AWS Tagging Strategies - Overview of how to use tagging to organize and track AWS resources.
AWS Cost Explorer - Details on how to use Cost Explorer to analyze costs.
A company is migrating its on-premises Oracle database to an Amazon RDS for Oracle database. The company needs to retain data for 90 days to meet regulatory requirements. The company must also be able to restore the database to a specific point in time for up to 14 days.
Which solution will meet these requirements with the LEAST operational overhead?
AWS Backup is the most appropriate solution for managing backups with minimal operational overhead while meeting the regulatory requirement to retain data for 90 days and enabling point-in-time restore for up to 14 days.
AWS Backup: AWS Backup provides a centralized backup management solution that supports automated backup scheduling, retention management, and compliance reporting across AWS services, including Amazon RDS. By creating a backup plan, you can define a retention period (in this case, 90 days) and automate the backup process.
Point-in-Time Restore (PITR): Amazon RDS supports point-in-time restore for up to 35 days with automated backups. By using AWS Backup in conjunction with RDS, you ensure that your backup strategy meets the requirement for restoring data to a specific point in time within the last 14 days.
Why Not Other Options?:
Option A (RDS Automated Backups): While RDS automated backups support PITR, they do not directly support retention beyond 35 days without manual intervention.
Option B (Manual Snapshots): Manually creating and managing snapshots is operationally intensive and less automated compared to AWS Backup.
Option C (Aurora Clones): Aurora Clone is a feature specific to Amazon Aurora and is not applicable to Amazon RDS for Oracle.
AWS Reference:
AWS Backup - Overview of AWS Backup and its capabilities.
Amazon RDS Automated Backups - Information on how RDS automated backups work and their limitations.
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