- (Exam Topic 3)
A company has application services that have been containerized and deployed on multiple Amazon EC2 instances with public IPs. An Apache Kafka cluster has been deployed to the EC2 instances. A PostgreSQL database has been migrated to Amazon RDS for PostgreSQL. The company expects a significant increase of orders on its platform when a new version of its flagship product is released.
What changes to the current architecture will reduce operational overhead and support the product release?

1. A. Create an EC2 Auto Scaling group behind an Application Load Balance
2. B. Create additional read replicas for the DB instanc
3. C. Create Amazon Kinesis data streams and configure the application services to use the data stream
4. D. Store and serve static content directly from Amazon S3.
5. E. Create an EC2 Auto Scaling group behind an Application Load Balance
6. F. Deploy the DB instance in Multi-AZ mode and enable storage auto scalin
7. G. Create Amazon Kinesis data streams and configure the application services to use the data stream
8. H. Store and serve static content directly from Amazon S3.
9. I. Deploy the application on a Kubernetes cluster created on the EC2 instances behind an Application Load Balance
10. J. Deploy the DB instance in Multi-AZ mode and enable storage auto scalin
11. K. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluste
12. L. Store static content in Amazon S3 behind an Amazon CloudFront distribution.
13. M. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balance
14. N. Create additional read replicas for the DB instanc
15. O. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluste
16. P. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

Correct Answer: D
The correct answer is D. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balancer. Create additional read replicas for the DB instance. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.
Option D meets the requirements of the scenario because it allows you to reduce operational overhead and support the product release by using the following AWS services and features:
Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed service that allows you to run Kubernetes applications on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use Amazon EKS to deploy your containerized application services on a Kubernetes cluster that is compatible with your existing tools and processes.
AWS Fargate is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can use AWS Fargate as the launch type for your Amazon EKS pods, which are the smallest deployable units of computing in Kubernetes. You can also enable auto scaling for your pods, which allows you to automatically adjust the number of pods based on the demand or custom metrics.
An Application Load Balancer (ALB) is a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use an ALB to balance the load across your Amazon EKS pods and provide high availability and fault tolerance for your application.
Amazon RDS for PostgreSQL is a fully managed relational database service that supports the PostgreSQL open source database engine. You can create additional read replicas for your DB instance, which are copies of your primary DB instance that can handle read-only queries and improve performance. You can also use read replicas to scale out beyond the capacity of a single DB instance for read-heavy workloads.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open source platform for building real-time data pipelines and streaming applications. You can use Amazon MSK to create and manage a Kafka cluster that is highly available, secure, and compatible with your existing Kafka applications. You can also configure your application services to use the Amazon MSK cluster as a source or destination of streaming data.
Amazon S3 is an object storage service that offers high durability, availability, and scalability. You can store static content such as images, videos, or documents in Amazon S3 buckets, which are containers for objects. You can also serve static content directly from Amazon S3 using public URLs or presigned URLs.
Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. You can use Amazon CloudFront to create a distribution that caches static content from your Amazon S3 bucket at edge locations closer to your users. This can improve the performance and user experience of your application.
Option A is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating additional read replicas for the DB instance would not provide
high availability or fault tolerance in case of a failure of the primary DB instance, unlike deploying the DB instance in Multi-AZ mode. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK.
Option B is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK. Storing and serving static content directly from Amazon S3 would not provide optimal performance and user experience, unlike using Amazon CloudFront.
Option C is incorrect because deploying the application on a Kubernetes cluster created on the EC2 instances behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances and Kubernetes control plane for your containers. Using Amazon API Gateway to interact with the application would add an unnecessary layer of complexity and cost to your architecture, as you would need to create and maintain an API gateway that proxies requests to your ALB.

- (Exam Topic 1)
A company has migrated an application from on premises to AWS. The application frontend is a static website that runs on two Amazon EC2 instances behind an Application Load Balancer (ALB). The application backend is a Python application that runs on three EC2 instances behind another ALB. The EC2 instances are large, general purpose On-Demand Instances that were sized to meet the on-premises specifications for peak usage of the application.
The application averages hundreds of thousands of requests each month. However, the application is used mainly during lunchtime and receives minimal traffic during the rest of the day.
A solutions architect needs to optimize the infrastructure cost of the application without negatively affecting the application availability.
Which combination of steps will meet these requirements? (Choose two.)

1. A. Change all the EC2 instances to compute optimized instances that have the same number of cores as the existing EC2 instances.
2. B. Move the application frontend to a static website that is hosted on Amazon S3.
3. C. Deploy the application frontend by using AWS Elastic Beanstal
4. D. Use the same instance type for the nodes.
5. E. Change all the backend EC2 instances to Spot Instances.
6. F. Deploy the backend Python application to general purpose burstable EC2 instances that have the same number of cores as the existing EC2 instances.

Correct Answer: BD
Moving the application frontend to a static website that is hosted on Amazon S3 will save cost as S3 is cheaper than running EC2 instances.
Using Spot instances for the backend EC2 instances will also save cost, as they are significantly cheaper than On-Demand instances. This will be suitable for the application, as it has minimal traffic during the rest of the day, and the availability of spot instances will not negatively affect the application's availability.
Reference:
Amazon S3 pricing: https://aws.amazon.com/s3/pricing/
Amazon EC2 Spot Instances documentation: https://aws.amazon.com/ec2/spot/ AWS Elastic Beanstalk documentation: https://aws.amazon.com/elasticbeanstalk/ Amazon Elastic Compute Cloud (EC2) pricing: https://aws.amazon.com/ec2/pricing/

- (Exam Topic 2)
A company is providing weather data over a REST-based API to several customers. The API is hosted by Amazon API Gateway and is integrated with different AWS Lambda functions for each API operation. The company uses Amazon Route 53 for DNS and has created a resource record of weather.example.com. The company stores data for the API in Amazon DynamoDB tables. The company needs a solution that will give the API the ability to fail over to a different AWS Region.
Which solution will meet these requirements?

1. A. Deploy a new set of Lambda functions in a new Regio
2. B. Update the API Gateway API to use an edge-optimized API endpoint with Lambda functions from both Regions as target
3. C. Convert the DynamoDB tables to global tables.
4. D. Deploy a new API Gateway API and Lambda functions in another Regio
5. E. Change the Route 53 DNS record to a multivalue answe
6. F. Add both API Gateway APIs to the answe
7. G. Enable target health monitorin
8. H. Convert the DynamoDB tables to global tables.
9. I. Deploy a new API Gateway API and Lambda functions in another Regio
10. J. Change the Route 53 DNS record to a failover recor
11. K. Enable target health monitorin
12. L. Convert the DynamoDB tables to global tables.
13. M. Deploy a new API Gateway API in a new Regio
14. N. Change the Lambda functions to global functions.Change the Route 53 DNS record to a multivalue answe
15. O. Add both API Gateway APIs to the answe
16. P. Enable target health monitorin
17. Q. Convert the DynamoDB tables to global tables.

Correct Answer: C
https://docs.aws.amazon.com/apigateway/latest/developerguide/dns-failover.html

- (Exam Topic 2)
A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day.
The company needs to query and analyze the data. The company does not access data that is more than 1 year old. However, the company must retain all the data indefinitely for compliance reasons.
Which solution will meet these requirements MOST cost-effectively?

1. A. Use S3 Select to query the dat
2. B. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
3. C. Use Amazon Redshift Spectrum to query the dat
4. D. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
5. E. Use an AWS Glue Data Catalog and Amazon Athena to query the dat
6. F. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
7. G. Use Amazon Redshift Spectrum to query the dat
8. H. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Intelligent-Tiering.

Correct Answer: C
Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html

- (Exam Topic 1)
A company has an on-premises monitoring solution using a PostgreSQL database for persistence of events. The database is unable to scale due to heavy ingestion and it frequently runs out of storage.
The company wants to create a hybrid solution and has already set up a VPN connection between its network and AWS. The solution should include the following attributes:
• Managed AWS services to minimize operational complexity
• A buffer that automatically scales to match the throughput of data and requires no on-going administration.
• A visualization toot to create dashboards to observe events in near-real time.
• Support for semi -structured JSON data and dynamic schemas.
Which combination of components will enabled© company to create a monitoring solution that will satisfy these requirements'' (Select TWO.)

1. A. Use Amazon Kinesis Data Firehose to buffer events Create an AWS Lambda function 10 process and transform events
2. B. Create an Amazon Kinesis data stream to buffer events Create an AWS Lambda function to process and transform evens
3. C. Configure an Amazon Aurora PostgreSQL DB cluster to receive events Use Amazon Quick Sight to read from the database and create near-real-time visualizations and dashboards
4. D. Configure Amazon Elasticsearch Service (Amazon ES) to receive events Use the Kibana endpoint deployed with Amazon ES to create near-real-time visualizations and dashboards.
5. E. Configure an Amazon Neptune 0 DB instance to receive events Use Amazon QuickSight to read from the database and create near-real-time visualizations and dashboards

Correct Answer: AD
https://aws.amazon.com/kinesis/data-firehose/faqs/