Q81 — AWS SAA-C03 Ch.14

Question 81 of 100 | ← Chapter 14

QuickSight offers powerful visualization capabilities.Suitability:DynamoDB is generally used for transactional workloads rather than time series data. Ingesting data from DynamoDB into EMR may introduce latency and complexity. While feasible, this solution may not be as optimized for real-time telemetry and ML processing as Option A.C. Use Amazon Neptune to store the data points. Use Amazon Kinesis Data Streams to ingest data from Neptune into an AWS Lambda function for processing. Use Amazon QuickSight to visualize the data.Advantages:Amazon Neptune is a fully managed graph database service.Kinesis Data Streams allows real-time data streaming.AWS Lambda can process data streams in real-time.QuickSight provides visualization capabilities.Suitability:Neptune is designed for graph data, which is not suitable for telemetry data from vehicles. While Kinesis and Lambda can handle real-time data, the mismatch with Neptune makes this solution less optimal.D. Use Amazon Timestream for LiveAnalytics to store the data points. Grant Amazon SageMaker permission to access the data for processing. Use Amazon Athena to visualize the data.Advantages:Timestream and SageMaker are well-suited for time series data and ML processing, respectively. Amazon Athena allows querying data in Amazon S3 using standard SQL.Suitability:Athena is typically used for querying data stored in S3 rather than directly visualizing it. For visualization, QuickSight would be a more suitable choice than Athena.Conclusion:Based on the analysis, the most suitable solution is:

Correct Answer: A. Use Amazon Timestream for LiveAnalytics to store the data points. Grant Amazon SageMaker permission to access the data for processing. Use Amazon QuickSight to visualize the data.This solution effectively addresses the company's needs for storing, processing, and visualizing large volumes of telemetry data from its fleet of vehicles. Amazon Timestream is optimized for time series data, Amazon SageMaker provides powerful ML capabilities, and Amazon QuickSight offers robust visualization tools.Q1081. A company is developing software that uses a PostgreSQL database schema. The company needs to configure development environments and test environments for its developers.Each developer at the company uses their own development environment, which includes a PostgreSQL database. On average, each development environment is used for an 8-hour workday. The test environments will be used for load testing that can take up to 2 hours each day.Which solution will meet these requirements MOST cost-effectively?

Explanation

To determine the most cost-effective solution for configuring development and test environments for a company using a PostgreSQL database schema, let's evaluate each option based on the provided usage patterns:Requirements Recap:Each developer has their own development environment with a PostgreSQL database. Development environments are used for an average of 8 hours per day. Test environments are used for load testing that can take up to 2 hours each day.Option Analysis:A. Configure development environments and test environments with their own Amazon Aurora Serverless v2 PostgreSQL database.Advantages:Aurora Serverless v2 automatically scales capacity based on demand.No need to manage database capacity manually.Pay only for the capacity used.Cost Considerations:Cost is proportional to the actual usage, which can be very efficient for intermittent use cases like development and testing.Automatically scales down when not in use, reducing wasted capacity costs. B. For each development environment, configure an Amazon RDS for PostgreSQL Single-AZ DB instance. For the test environment, configure a single Amazon RDS for PostgreSQL Multi-AZ DB instance.Advantages:RDS provides a managed database service with automated backups, patching, etc. Multi-AZ deployment for test environment ensures high availability.Cost Considerations:Cost is fixed based on the instance type and size, regardless of actual usage. Single-AZ instances may be cheaper but lack the high availability of Multi-AZ.Multi-AZ deployment adds redundancy but increases cost.C. Configure development environments and test environments with their own Amazon Aurora PostgreSQL DB cluster.Advantages:Aurora provides high performance and availability.Each environment has its own isolated cluster.Cost Considerations:Cost is based on the cluster size and configuration, which may be excessive for intermittent use cases. May lead to underutilization and higher costs during off-peak hours. D. Configure an Amazon Aurora global database. Allow developers to connect to the database with their own credentials.Advantages:Global database provides cross-region replication and high availability.Centralized management and monitoring.Cost Considerations:Significantly higher cost due to the complexity and redundancy of a global database setup. Not suitable for isolated development and test environments where each developer needs their own database instance.Conclusion:Based on the analysis, the most cost-effective solution is:A. Configure development environments and test environments with their own Amazon Aurora Serverless v2 PostgreSQL database.This option leverages Aurora Serverless v2's ability to automatically scale based on demand, ensuring that the company only pays for the capacity actually used. This is particularly beneficial for development and test environments, which have intermittent usage patterns. Aurora Serverless v2 also eliminates the need for manual capacity management, reducing operational overhead.