Amazon Web Services Updated SAA-C03 Exam Questions and Answers by sufyan

A. Aurora MySQL: Handles OLTP workloads efficiently with built-in replication and auto-scaling capabilities.

B. EC2 with MySQL: Requires heavy manual maintenance and does not scale seamlessly.

C. RDS for MySQL: Limited in auto-scaling compared to Aurora.

D. Redshift: Primarily for OLAP, not suitable for OLTP workloads.

References: Amazon Aurora

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Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower.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 References:

Amazon Aurora Serverless - Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing - Details on the cost-effectiveness of Aurora Serverless.

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

References:

AWS Lake Formation

Tag-based access control

EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.

Option A: Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.

Option C: Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.

Option D: Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.

AWS Documentation References:

EMR Managed Scaling