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| Section | Weight | Objectives |
|---|---|---|
| Design Cost-Optimized Architectures | 20% | - Design cost-optimized database solutions
|
| Design Secure Architectures | 30% | - Design secure access to AWS resources
|
| Design Resilient Architectures | 26% | - Design highly available and/or fault-tolerant architectures
|
| Design High-Performing Architectures | 24% | - Determine high-performing data ingestion and transformation solutions
|
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NEW QUESTION # 391
A company is building an Amazon Elastic Kubernetes Service (Amazon EKS) cluster for its workloads. All secrets that are stored in Amazon EKS must be encrypted in the Kubernetes etcd key-value store.
Which solution will meet these requirements?
Answer: D
Explanation:
This option is the most secure and simple way to encrypt the secrets that are stored in Amazon EKS. AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys that can be used to encrypt your data. Amazon EKS KMS secrets encryption is a feature that enables you to use a KMS key to encrypt the secrets that are stored in the Kubernetes etcd key-value store. This provides an additional layer of protection for your sensitive data, such as passwords, tokens, and keys. You can create a new KMS key or use an existing one, and then enable the Amazon EKS KMS secrets encryption on the Amazon EKS cluster. You can also use IAM policies to control who can access or use the KMS key.
Option A is not correct because using AWS Secrets Manager to manage, rotate, and store all secrets in Amazon EKS is not necessary or efficient. AWS Secrets Manager is a service that helps you securely store, retrieve, and rotate your secrets, such as database credentials, API keys, and passwords. You can use it to manage secrets that are used by your applications or services outside of Amazon EKS, but it is not designed to encrypt the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using AWS Secrets Manager would incur additional costs and complexity, and it would not leverage the native Kubernetes secrets management capabilities.
Option C is not correct because using the Amazon EBS Container Storage Interface (CSI) driver as an add-on does not encrypt the secrets that are stored in Amazon EKS. The Amazon EBS CSI driver is a plugin that allows you to use Amazon EBS volumes as persistent storage for your Kubernetes pods. It is useful for providing durable and scalable storage for your applications, but it does not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the Amazon EBS CSI driver would require additional configuration and resources, and it would not provide the same level of security as using a KMS key.
Option D is not correct because creating a new AWS KMS key with the alias aws/ebs and enabling default Amazon EBS volume encryption for the account does not encrypt the secrets that are stored in Amazon EKS.
The alias aws/ebs is a reserved alias that is used by AWS to create a default KMS key for your account. This key is used to encrypt the Amazon EBS volumes that are created in your account, unless you specify a different KMS key. Enabling default Amazon EBS volume encryption for the account is a setting that ensures that all new Amazon EBS volumes are encrypted by default. However, these features do not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the default KMS key or the default encryption setting would not provide the same level of control and security as using a custom KMS key and enabling the Amazon EKS KMS secrets encryption feature. References:
Encrypting secrets used in Amazon EKS
What Is AWS Key Management Service?
What Is AWS Secrets Manager?
Amazon EBS CSI driver
Encryption at rest
NEW QUESTION # 392
A company's SAP application has a backend SQL Server database in an on-premises environment. The company wants to migrate its on-premises application and database server to AWS. The company needs an instance type that meets the high demands of its SAP database.
On-premises performance data shows that both the SAP application and the database have high memory utilization. Which solution will meet these requirements?
Answer: B
Explanation:
Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization. High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on- premises performance data. Memory optimized instances provide the necessary memory capacity and performance.
Instance Types:
For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.
For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.
Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.
NEW QUESTION # 393
A company is concerned about the security of its public web application due to recent web attacks. The application uses an Application Load Balancer (ALB). A solutions architect must reduce the risk of DDoS attacks against the application.
What should the solutions architect do to meet this requirement?
Answer: D
NEW QUESTION # 394
[Design Secure Architectures]
A company runs container applications by using Amazon Elastic Kubernetes Service (Amazon EKS). The company's workload is not consistent throughout the day The company wants Amazon EKS to scale in and out according to the workload.
Which combination of steps will meet these requirements with the LEAST operational overhead? {Select TWO.)
Answer: D,E
Explanation:
https://docs.aws.amazon.com/eks/latest/userguide/horizontal-pod-autoscaler.html
https://docs.aws.amazon.com/eks/latest/userguide/autoscaling.html
Horizontal pod autoscaling is a feature of Kubernetes that automatically scales the number of pods in a deployment, replication controller, or replica set based on that resource's CPU utilization. It requires ametrics source such as the Kubernetes Metrics Server to provide CPU usage data1. Cluster autoscaling is a feature of Kubernetes that automatically adjusts the number of nodes in a cluster when pods fail or are rescheduled onto other nodes. It requires an integration with AWS Auto Scaling groups to manage the EC2 instances that join the cluster2. By using both horizontal pod autoscaling and cluster autoscaling, the solution can ensure that Amazon EKS scales in and out according to the workload.
NEW QUESTION # 395
An online cryptocurrency exchange platform is hosted in AWS which uses ECS Cluster and RDS in Multi-AZ Deployments configuration. The application is heavily using the RDS instance to process complex read and write database operations. To maintain the reliability, availability, and performance of your systems, you have to closely monitor how the different processes or threads on a DB instance use the CPU, including the percentage of the CPU bandwidth and total memory consumed by each process.
Which of the following is the most suitable solution to properly monitor your database?
Answer: A
Explanation:
Amazon RDS provides metrics in real time for the operating system (OS) that your DB instance runs on.
You can view the metrics for your DB instance using the console, or consume the Enhanced Monitoring JSON output from CloudWatch Logs in a monitoring system of your choice. By default, Enhanced Monitoring metrics are stored in the CloudWatch Logs for 30 days. To modify the amount of time the metrics are stored in the CloudWatch Logs, change the retention for the RDSOSMetrics log group in the CloudWatch console.
Take note that there are certain differences between CloudWatch and Enhanced Monitoring Metrics.
CloudWatch gathers metrics about CPU utilization from the hypervisor for a DB instance, and Enhanced Monitoring gathers its metrics from an agent on the instance. As a result, you might find differences between the measurements, because the hypervisor layer performs a small amount of work. Hence, enabling Enhanced Monitoring in RDS is the correct answer in this specific scenario.
The differences can be greater if your DB instances use smaller instance classes, because then there are likely more virtual machines (VMs) that are managed by the hypervisor layer on a single physical instance. Enhanced Monitoring metrics are useful when you want to see how different processes or threads on a DB instance use the CPU.
Using Amazon CloudWatch to monitor the CPU Utilization of your database is incorrect. Although you can use this to monitor the CPU Utilization of your database instance, it does not provide the percentage of the CPU bandwidth and total memory consumed by each database process in your RDS instance.
Take note that CloudWatch gathers metrics about CPU utilization from the hypervisor for a DB instance while RDS Enhanced Monitoring gathers its metrics from an agent on the instance.
The option that says: Create a script that collects and publishes custom metrics to CloudWatch, which tracks the real-time CPU Utilization of the RDS instance and then set up a custom CloudWatch dashboard to view the metrics is incorrect. Although you can use Amazon CloudWatch Logs and CloudWatch dashboard to monitor the CPU Utilization of the database instance, using CloudWatch alone is still not enough to get the specific percentage of the CPU bandwidth and total memory consumed by each database processes. The data provided by CloudWatch is not as detailed as compared with the Enhanced Monitoring feature in RDS. Take note as well that you do not have direct access to the instances/servers of your RDS database instance, unlike with your EC2 instances where you can install a CloudWatch agent or a custom script to get CPU and memory utilization of your instance.
The option that says: Check the CPU% and MEM% metrics which are readily available in the Amazon RDS console that shows the percentage of the CPU bandwidth and total memory consumed by each database process of your RDS instance is incorrect because the CPU% and MEM% metrics are not readily available in the Amazon RDS console, which is contrary to what is being stated in this option.
References:
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_Monitoring.OS.html#USER_Monitor ing.OS.CloudWatchLogs
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/MonitoringOverview.html#monitoring-cloud watch Check out this Amazon CloudWatch Cheat Sheet: https://tutorialsdojo.com/amazon-cloudwatch/ Check out this Amazon RDS Cheat Sheet: https://tutorialsdojo.com/amazon-relational-database-service- amazon-rds/
NEW QUESTION # 396
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