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| Section | Weight | Objectives |
|---|---|---|
| Design Cost-Optimized Architectures | 20% | - Control and monitor costs
|
| Design High-Performing Architectures | 24% | - Design high-performance compute solutions
|
| Design Resilient Architectures | 26% | - Design decoupled and loosely coupled systems
|
| Design Secure Architectures | 30% | - Design secure workloads and applications
|
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NEW QUESTION # 692
An online shopping platform is hosted on an Auto Scaling group of Spot EC2 instances and uses Amazon Aurora PostgreSQL as its database. There is a requirement to optimize your database workloads in your cluster where you have to direct the write operations of the production traffic to your high-capacity instances and point the reporting queries sent by your internal staff to the low-capacity instances.
Which is the most suitable configuration for your application as well as your Aurora database cluster to achieve this requirement?
Answer: C
Explanation:
Amazon Aurora typically involves a cluster of DB instances instead of a single instance. Each connection is handled by a specific DB instance. When you connect to an Aurora cluster, the host name and port that you specify point to an intermediate handler called an endpoint. Aurora uses the endpoint mechanism to abstract these connections. Thus, you don't have to hardcode all the hostnames or write your own logic for load-balancing and rerouting connections when some DB instances aren't available.
For certain Aurora tasks, different instances or groups of instances perform different roles. For example, the primary instance handles all data definition language (DDL) and data manipulation language (DML) statements. Up to 15 Aurora Replicas handle read-only query traffic.
Using endpoints, you can map each connection to the appropriate instance or group of instances based on your use case. For example, to perform DDL statements you can connect to whichever instance is the primary instance. To perform queries, you can connect to the reader endpoint, with Aurora automatically performing load-balancing among all the Aurora Replicas. For clusters with DB instances of different capacities or configurations, you can connect to custom endpoints associated with different subsets of DB instances. For diagnosis or tuning, you can connect to a specific instance endpoint to examine details about a specific DB instance.
The custom endpoint provides load-balanced database connections based on criteria other than the read-only or read-write capability of the DB instances. For example, you might define a custom endpoint to connect to instances that use a particular AWS instance class or a particular DB parameter group.
Then you might tell particular groups of users about this custom endpoint. For example, you might direct internal users to low-capacity instances for report generation or ad hoc (one-time) querying, and direct production traffic to high-capacity instances. Hence, creating a custom endpoint in Aurora based on the specified criteria for the production traffic and another custom endpoint to handle the reporting queries is the correct answer.
Configuring your application to use the reader endpoint for both production traffic and reporting queries, which will enable your Aurora database to automatically perform load-balancing among all the Aurora Replicas is incorrect. Although it is true that a reader endpoint enables your Aurora database to automatically perform load-balancing among all the Aurora Replicas, it is quite limited to doing read operations only. You still need to use a custom endpoint to load-balance the database connections based on the specified criteria.
The option that says: In your application, use the instance endpoint of your Aurora database to handle the incoming production traffic and use the cluster endpoint to handle reporting queries is incorrect because a cluster endpoint (also known as a writer endpoint) for an Aurora DB cluster simply connects to the current primary DB instance for that DB cluster. This endpoint can perform write operations in the database such as DDL statements, which is perfect for handling production traffic but not suitable for handling queries for reporting since there will be no write database operations that will be sent.
Moreover, the endpoint does not point to lower-capacity or high-capacity instances as per the requirement. A better solution for this is to use a custom endpoint.
The option that says: Do nothing since by default, Aurora will automatically direct the production traffic to your high-capacity instances and the reporting queries to your low-capacity instances is incorrect because Aurora does not do this by default. You have to create custom endpoints in order to accomplish this requirement.
Explanation:
Reference:
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.Endpoints.html Amazon Aurora Overview:
https://youtu.be/iwS1h7rLNBQ
Check out this Amazon Aurora Cheat Sheet:
https://tutorialsdojo.com/amazon-aurora/
NEW QUESTION # 693
An online gaming company is transitioning user data storage to Amazon DynamoDB to support the company's growing user base. The current architecture includes DynamoDB tables that contain user profiles, achievements, and in-game transactions.
The company needs to design a robust, continuously available, and resilient DynamoDB architecture to maintain a seamless gaming experience for users.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
DynamoDB Global Tables provide a fully managed, multi-region, and multi-master database solution that allows you to deploy DynamoDB tables in multiple AWS Regions. This ensures high availability and resiliency across different geographical locations, providing a seamless gaming experience for users. Using provisioned capacity mode with auto-scaling ensures cost-efficiency by scaling up or down based on actual demand.
* Option A: While on-demand capacity mode is flexible, provisioned capacity with auto-scaling is more cost-effective for predictable workloads.
* Option B (DAX): DAX improves read performance, but it doesn't provide the multi-region replication needed for high availability and resiliency.
* Option C: DynamoDB Streams with manual cross-region replication adds more complexity and operational overhead compared to Global Tables.
AWS References:
* DynamoDB Global Tables
NEW QUESTION # 694
A company has migrated an application to Amazon EC2 Linux instances. One of these EC2 instances runs several 1-hour tasks on a schedule. These tasks were written by different teams and have no common programming language. The company is concerned about performance and scalability while these tasks run on a single instance. A solutions architect needs to implement a solution to resolve these concerns.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
AWS Batch is a fully managed service that enables users to run batch jobs on AWS. It can handle different types of tasks written in different languages and run them on EC2 instances. It also integrates with Amazon EventBridge (Amazon CloudWatch Events) to schedule jobs based on time or event triggers. This solution will meet the requirements of performance, scalability and low operational overhead12.
1. Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs. This solution will not meet the requirement of low operational overhead, as it involves converting the EC2 instance to a container and using AWS App Runner, which is a service that automatically builds and deploys web applications and load balances traffic2. This is not necessary for running batch jobs.
2. Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events). This solution will not meet the requirement of performance, as AWS Lambda has a limit of 15 minutes for execution time and 10 GB for memory allocation3. These limits may not be sufficient for running 1-hour tasks.
3. Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance. This solution will not meet the requirement of low operational overhead, as it involves creating and maintaining AMIs and Auto Scaling groups, which are additional resources that need to be configured and managed2.
Reference URL: https://docs.aws.amazon.com/whitepapers/latest/aws-overview/compute-services.html
NEW QUESTION # 695
A company has a large fleet of vehicles that are equipped with internet connectivity to send telemetry to the company. The company receives over 1 million data points every 5 minutes from the vehicles. The company uses the data in machine learning (ML) applications to predict vehicle maintenance needs and to preorder parts. The company produces visual reports based on the captured data. The company wants to migrate the telemetry ingestion, processing, and visualization workloads to AWS. Which solution will meet these requirements?
Answer: A
Explanation:
Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.
Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.
Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.
Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.
NEW QUESTION # 696
[Design Secure Architectures]
A solution architect needs to assign a new microsoft for a company's application. Clients must be able to call an HTTPS endpoint to reach the micoservice. The microservice also must use AWS identity and Access Management (IAM) to authentication calls. The soltions architect will write the logic for this microservice by using a single AWS Lambda function that is written in Go 1.x.
Which solution will deploy the function in the in the MOST operationally efficient way?
Answer: B
Explanation:
A . Create an Amazon API Gateway REST API. Configure the method to use the Lambda function. Enable IAM authentication on the API. This option is the most operationally efficient as it allows you to use API Gateway to handle the HTTPS endpoint and also allows you to useIAM to authenticate the calls to the microservice. API Gateway also provides manyadditional features such as caching, throttling, and monitoring, which can be useful for a microservice.
NEW QUESTION # 697
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