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NEW QUESTION 27
A business’s backup data totals 700 terabytes (TB) and is kept in network attached storage (NAS) at its data center. This backup data must be available in the event of occasional regulatory inquiries and preserved for a period of seven years. The organization has chosen to relocate its backup data from its on-premises data center to Amazon Web Services (AWS). Within one month, the migration must be completed. The company’s public internet connection provides 500 Mbps of dedicated capacity for data transport.
What should a solutions architect do to ensure that data is migrated and stored at the LOWEST possible cost?

  • A. Use AWS DataSync to transfer the data and deploy a DataSync agent on premises. Use the DataSync task to copy files from the on-premises NAS storage to Amazon S3 Glacier.
  • B. Order AWS Snowball devices to transfer the data. Use a lifecycle policy to transition the files to Amazon S3 Glacier Deep Archive.
  • C. Deploy a VPN connection between the data center and Amazon VPC. Use the AWS CLI to copy the data from on premises to Amazon S3 Glacier.
  • D. Provision a 500 Mbps AWS Direct Connect connection and transfer the data to Amazon S3. Use a lifecycle policy to transition the files to Amazon S3 Glacier Deep Archive.

Answer: B

 

NEW QUESTION 28
A technology company has a suite of container-based web applications and serverless solutions that are hosted in AWS. The Solutions Architect must define a standard infrastructure that will be used across development teams and applications. There are application-specific resources too that change frequently, especially during the early stages of application development. Developers must be able to add supplemental resources to their applications, which are beyond what the architects predefined in the system environments and service templates.
Which of the following should be implemented to satisfy this requirement?

  • A. Use the Amazon EKS Anywhere service for deploying container applications and serverless solutions. Create a service instance for each application-specific resource.
  • B. Set up AWS Control Tower to automate container-based application deployments. Use AWS Config for application-specific resources that change frequently.
  • C. Use the Amazon Elastic Container Service (ECS) Anywhere service for deploying container applications and serverless solutions. Configure Prometheus metrics collection on the ECS cluster and use Amazon Managed Service for Prometheus for monitoring frequently-changing resources
  • D. Set up AWS Proton for deploying container applications and serverless solutions. Create components from the AWS Proton console and attach them to their respective service instance.

Answer: D

Explanation:
AWS Proton allows you to deploy any serverless or container-based application with increased efficiency, consistency, and control. You can define infrastructure standards and effective continuous delivery pipelines for your organization. Proton breaks down the infrastructure into environment and service (“infrastructure as code” templates).
As a developer, you select a standardized service template that AWS Proton uses to create a service that deploys and manages your application in a service instance. An AWS Proton service is an instantiation of a service template, which normally includes several service instances and a pipeline.


The diagram above displays the high-level overview of a simple AWS Proton workflow.
In AWS Proton administrators define standard infrastructure that is used across development teams and applications. However, development teams might need to include additional resources for their specific use cases, like Amazon Simple Queue Service (Amazon SQS) queues or Amazon DynamoDB tables.
These application-specific resources might change frequently, particularly during early application development. Maintaining these frequent changes in administrator-authored templates might be hard to manage and scale-administrators would need to maintain many more templates without real administrator added value. The alternative-letting application developers author templates for their applications-isn’t ideal either, because it takes away administrators’ ability to standardize the main architecture components, like AWS Fargate tasks. This is where components come in.
With a component, a developer can add supplemental resources to their application, above and beyond what administrators defined in environment and service templates. The developer then attaches the component to a service instance. AWS Proton provisions infrastructure resources defined by the component just like it provisions resources for environments and service instances.
Hence, the correct answer is: Set up AWS Proton for deploying container applications and serverless solutions. Create components from the AWS Proton console and attach them to their respective service instance.
The option that says: Use the Amazon EKS Anywhere service for deploying container applications and serverless solutions. Create a service instance for each application-specific resource is incorrect.
Amazon EKS Anywhere just allows you to manage a Kubernetes cluster on external environments that are supported by AWS. It is better to use AWS Proton with custom Components that can be attached to the different service instances of the company’s application suite.
The option that says: Set up AWS Control Tower to automate container-based application deployments.
Use AWS Config for application-specific resources that change frequently is incorrect. AWS Control Tower is used to simplify the creation of new accounts with preconfigured constraints. It isn’t used to automate application deployments. Moreover, AWS Config is commonly used for monitoring the changes of AWS resources and not the custom resources for serverless or container-based applications in AWS.
A combination of AWS Proton and Components is the most suitable solution for this scenario.
The option that says: Use the Amazon Elastic Container Service (ECS) Anywhere service for deploying container applications and serverless solutions. Configure Prometheus metrics collection on the ECS cluster and use Amazon Managed Service for Prometheus for monitoring frequently-changing resources is incorrect. The Amazon Managed Service for Prometheus is only a Prometheus-compatible monitoring and alerting service that makes it easy to monitor containerized applications and infrastructure at scale.
It is not capable of tracking or maintaining your application-specific resources that change frequently.
References:
https://docs.aws.amazon.com/proton/latest/userguide/Welcome.html
https://aws.amazon.com/blogs/architecture/simplifying-multi-account-ci-cd-deployments-using-aws-proto n/

 

NEW QUESTION 29
A company has a requirement to move 80 TB data warehouse to the cloud. It would take 2 months to transfer the data given their current bandwidth allocation.
Which is the most cost-effective service that would allow you to quickly upload their data into AWS?

  • A. AWS Snowball Edge
  • B. AWS Snowmobile
  • C. AWS Direct Connect
  • D. Amazon S3 Multipart Upload

Answer: A

Explanation:
AWS Snowball Edge is a type of Snowball device with on-board storage and compute power for select AWS capabilities. Snowball Edge can undertake local processing and edge-computing workloads in addition to transferring data between your local environment and the AWS Cloud.
Each Snowball Edge device can transport data at speeds faster than the internet. This transport is done by shipping the data in the appliances through a regional carrier. The appliances are rugged shipping containers, complete with E Ink shipping labels. The AWS Snowball Edge device differs from the standard Snowball because it can bring the power of the AWS Cloud to your on-premises location, with local storage and compute functionality.
Snowball Edge devices have three options for device configurations – storage optimized, compute optimized, and with GPU.
Hence, the correct answer is: AWS Snowball Edge.
AWS Snowmobile is incorrect because this is an Exabyte-scale data transfer service used to move extremely large amounts of data to AWS. It is not suitable for transferring a small amount of data, like 80 TB in this scenario. You can transfer up to 100PB per Snowmobile, a 45-foot long ruggedized shipping container, pulled by a semi-trailer truck. A more cost-effective solution here is to order a Snowball Edge device instead.
AWS Direct Connect is incorrect because it is primarily used to establish a dedicated network connection from your premises network to AWS. This is not suitable for one-time data transfer tasks, like what is depicted in the scenario.
Amazon S3 Multipart Upload is incorrect because this feature simply enables you to upload large objects in multiple parts. It still uses the same Internet connection of the company, which means that the transfer will still take time due to its current bandwidth allocation. References:
https://docs.aws.amazon.com/snowball/latest/ug/whatissnowball.html
https://docs.aws.amazon.com/snowball/latest/ug/device-differences.html Check out this AWS Snowball Edge Cheat Sheet: https://tutorialsdojo.com/aws-snowball-edge/ AWS Snow Family Overview:
https://youtu.be/9Ar-51Ip53Q

 

NEW QUESTION 30
A large telecommunications company needs to run analytics against all combined log files from the Application Load Balancer as part of the regulatory requirements.
Which AWS services can be used together to collect logs and then easily perform log analysis?

  • A. Amazon S3 for storing ELB log files and Amazon EMR for analyzing the log files.
  • B. Amazon EC2 with EBS volumes for storing and analyzing the log files.
  • C. Amazon DynamoDB for storing and EC2 for analyzing the logs.
  • D. Amazon S3 for storing the ELB log files and an EC2 instance for analyzing the log files using a custom-built application.

Answer: A

Explanation:
In this scenario, it is best to use a combination of Amazon S3 and Amazon EMR: Amazon S3 for storing ELB log files and Amazon EMR for analyzing the log files. Access logging in the ELB is stored in Amazon S3 which means that the following are valid options:
– Amazon S3 for storing the ELB log files and an EC2 instance for analyzing the log files using a custom- built application.
– Amazon S3 for storing ELB log files and Amazon EMR for analyzing the log files.
However, log analysis can be automatically provided by Amazon EMR, which is more economical than building a custom-built log analysis application and hosting it in EC2. Hence, the option that says:
Amazon S3 for storing ELB log files and Amazon EMR for analyzing the log files is the best answer between the two.
Access logging is an optional feature of Elastic Load Balancing that is disabled by default. After you enable access logging for your load balancer, Elastic Load Balancing captures the logs and stores them in the Amazon S3 bucket that you specify as compressed files. You can disable access logging at any time.

Amazon EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. It securely and reliably handles a broad set of big data use cases, including log analysis, web indexing, data transformations (ETL), machine learning, financial analysis, scientific simulation, and bioinformatics. You can also run other popular distributed frameworks such as Apache Spark, HBase, Presto, and Flink in Amazon EMR, and interact with data in other AWS data stores such as Amazon S3 and Amazon DynamoDB.
The option that says: Amazon DynamoDB for storing and EC2 for analyzing the logs is incorrect because DynamoDB is a noSQL database solution of AWS. It would be inefficient to store logs in DynamoDB while using EC2 to analyze them.
The option that says: Amazon EC2 with EBS volumes for storing and analyzing the log files is incorrect because using EC2 with EBS would be costly, and EBS might not provide the most durable storage for your logs, unlike S3.
The option that says: Amazon S3 for storing the ELB log files and an EC2 instance for analyzing the log files using a custom-built application is incorrect because using EC2 to analyze logs would be inefficient and expensive since you will have to program the analyzer yourself. References:
https://aws.amazon.com/emr/
https://docs.aws.amazon.com/elasticloadbalancing/latest/application/load-balancer-access-logs.html Check out this Amazon EMR Cheat Sheet:
https://tutorialsdojo.com/amazon-emr/
Check out this AWS Elastic Load Balancing (ELB) Cheat Sheet:
https://tutorialsdojo.com/aws-elastic-load-balancing-elb/

 

NEW QUESTION 31
An application runs on Amazon EC2 instances in private subnets. The application needs to access an Amazon DynamoDB table. What is the MOST secure way to access the table while ensuring that the traffic does not leave the AWS network?

  • A. Use the internet gateway attached to the VPC.
  • B. Use a NAT instance in a private subnet.
  • C. Use a NAT gateway in a public subnet.
  • D. Use a VPC endpoint for DynamoDB.

Answer: D

Explanation:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/vpc-endpoints-dynamodb.html A VPC endpoint for DynamoDB enables Amazon EC2 instances in your VPC to use their private IP addresses to access DynamoDB with no exposure to the public internet. Your EC2 instances do not require public IP addresses, and you don’t need an internet gateway, a NAT device, or a virtual private gateway in your VPC. You use endpoint policies to control access to DynamoDB. Traffic between your VPC and the AWS service does not leave the Amazon network.

 

NEW QUESTION 32
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