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Question 51

A financial services company has multiple AWS accounts hosting its portfolio of IT applications that serve the company's retail and enterprise customers. A CloudWatch Logs agent is installed on each of the EC2 instances running these IT applications. The company wants to aggregate all security events in a centralized AWS account dedicated to log storage. The centralized operations team at the company needs to perform near-real-time gathering and collating events across multiple AWS accounts.

As a Solutions Architect Professional, which of the following solutions would you suggest to meet these requirements?

Set up Kinesis Data Firehose in the logging account and then subscribe the delivery stream to CloudWatch Logs streams in each application AWS account via subscription filters. Persist the log data in an Amazon S3 bucket inside the logging AWS account

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Correct AnswerD

Correct option:

You can configure Amazon Kinesis Data Firehose to aggregate and collate CloudWatch Logs from different AWS accounts and receive their log events in a centralized logging AWS Account (this is known as cross-account data sharing) by using a CloudWatch Logs destination and then creating a Subscription Filter. This log event data can be read from a centralized Amazon Kinesis Firehose delivery stream to perform downstream processing and analysis.

You can collaborate with an owner of a different AWS account and receive their log events on your AWS resources, such as an Amazon Kinesis or Amazon Kinesis Data Firehose stream (this is known as cross-account data sharing). You can use a subscription filter with Kinesis Streams, Lambda, or Kinesis Data Firehose. Logs that are sent to a receiving service through a subscription filter are Base64 encoded and compressed with the gzip format.

Incorrect options:

Set up a new IAM role in each application AWS account with permissions to view CloudWatch Logs. Create a Lambda function to assume this new role and perform an hourly export of each AWS account's CloudWatch Logs data to an S3 bucket in the centralized logging AWS account - As the Lambda function is performing an hourly export, so it's not a near-real time soluton. In addition, Lambda is not the right choice to build a high volume and high-velocity streaming solution which is better handled by using the Kinesis Family of services.

Set up CloudWatch Logs agents to publish data to a Kinesis Data Firehose stream in the centralized logging AWS account. Create a Lambda function to read messages from the stream and push messages to Kinesis Data Firehose and then store the data in S3 - The CloudWatch Logs agent (on the path to deprecation) supports the collection of logs from only servers running Linux. It is recommended to use the unified CloudWatch agent. It enables you to collect both logs and advanced metrics with one agent. It offers support across operating systems, including servers running Windows Server. This agent also provides better performance. CloudWatch Logs agent cannot publish data to a Kinesis Data Firehose stream, so this option is incorrect.

Set up CloudWatch Logs streams in each application AWS account to forward events to CloudWatch Logs in the centralized logging AWS account. In the centralized logging AWS account, subscribe a Kinesis Data Firehose stream to Amazon CloudWatch Events and further use the Firehose stream to store the log data in S3 - You can use a subscription filter with Kinesis Streams, Lambda, or Kinesis Data Firehose. So you cannot just forward events directly to CloudWatch Logs in another account. In addition, Kinesis Data Firehose stream cannot subscribe to CloudWatch Events, so this option is incorrect.

References:

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CrossAccountSubscriptions.html

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

https://aws.amazon.com/blogs/architecture/stream-amazon-cloudwatch-logs-to-a-centralized-account-for-audit-and-analysis/

Question 52

A leading video creation and distribution company has recently migrated to AWS Cloud for digitally transforming its movie business. The company wants to speed up its media distribution process and improve data security while also reducing costs and eliminating errors. The company wants to set up a Digital Cinema Network that would allow it to store content in Amazon S3 as well as to accelerate the online distribution of movies and advertising to theaters in 38 key media markets worldwide. The company also wants to do an accelerated online migration of hundreds of terabytes of files from their on-premises data center to Amazon S3 and then establish a mechanism for low-latency access of the migrated data for ongoing updates from the on-premises applications.

As a Solutions Architect Professional, which of the following would you select as the MOST performant solution for the given use-case?

Use AWS DataSync to migrate existing data to Amazon S3 and then use File Gateway for low latency access to the migrated data for ongoing updates from the on-premises applications

Use AWS DataSync to first migrate existing data to Amazon S3 and then configure low latency access to the migrated data for ongoing updates from the on-premises applications

Use S3 Transfer Acceleration to migrate existing data to Amazon S3 and then use DataSync for low latency access to the migrated data for ongoing updates from the on-premises applications

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Correct AnswerA

Correct options:

Use AWS DataSync to migrate existing data to Amazon S3 and then use File Gateway for low latency access to the migrated data for ongoing updates from the on-premises applications

AWS DataSync is an online data transfer service that simplifies, automates, and accelerates copying large amounts of data to and from AWS storage services over the internet or AWS Direct Connect. AWS DataSync fully automates and accelerates moving large active datasets to AWS, up to 10 times faster than command-line tools. It is natively integrated with Amazon S3, Amazon EFS, Amazon FSx for Windows File Server, Amazon CloudWatch, and AWS CloudTrail, which provides seamless and secure access to your storage services, as well as detailed monitoring of the transfer.

DataSync uses a purpose-built network protocol and scale-out architecture to transfer data. A single DataSync agent is capable of saturating a 10 Gbps network link. DataSync fully automates the data transfer. It comes with retry and network resiliency mechanisms, network optimizations, built-in task scheduling, monitoring via the DataSync API and Console, and CloudWatch metrics, events, and logs that provide granular visibility into the transfer process. DataSync performs data integrity verification both during the transfer and at the end of the transfer.

How DataSync Works : https://aws.amazon.com/datasync/

AWS Storage Gateway is a hybrid cloud storage service that gives you on-premises access to virtually unlimited cloud storage. The service provides three different types of gateways – Tape Gateway, File Gateway, and Volume Gateway – that seamlessly connect on-premises applications to cloud storage, caching data locally for low-latency access. File gateway offers SMB or NFS-based access to data in Amazon S3 with local caching.

The combination of DataSync and File Gateway is the correct solution. AWS DataSync enables you to automate and accelerate online data transfers to AWS storage services. File Gateway then provides your on-premises applications with low latency access to the migrated data.

AWS File Gateway : https://aws.amazon.com/storagegateway/file/

Incorrect options:

Use AWS DataSync to first migrate existing data to Amazon S3 and then configure low latency access to the migrated data for ongoing updates from the on-premises applications - AWS DataSync is used to easily transfer data to and from AWS with up to 10x faster speeds. It is used to transfer data and should not be used for low latency access to the migrated data for ongoing updates from the on-premises applications.

Use File Gateway configuration of AWS Storage Gateway to migrate data to Amazon S3 and then use S3 Transfer Acceleration for low latency access to the migrated data for ongoing updates from the on-premises applications - File Gateway can be used to move on-premises data to AWS Cloud, but it not an optimal solution for high volumes. Migration services such as DataSync are best suited for this purpose. S3 Transfer Acceleration cannot facilitate low latency access to the migrated data for ongoing updates from the on-premises applications.

Use S3 Transfer Acceleration to migrate existing data to Amazon S3 and then use DataSync for low latency access to the migrated data for ongoing updates from the on-premises applications - If your application is already integrated with the Amazon S3 API, and you want higher throughput for transferring large files to S3, S3 Transfer Acceleration can be used. However, DataSyncshould not be used for low latency access to the migrated data for ongoing updates from the on-premises applications.

References:

https://aws.amazon.com/datasync/features/

https://aws.amazon.com/storagegateway/file/

https://aws.amazon.com/datasync/faqs/

Question 53

A mobile app based social media company is using Amazon CloudFront to deliver media-rich content to its audience across the world. The Content Delivery Network (CDN) offers a multi-tier cache by default, with regional edge caches that improve latency and lower the load on the origin servers when the object is not already cached at the edge. However, there are certain content types that bypass the regional edge cache and go directly to the origin.

Which of the following content types skip the regional edge cache? (Select two)

Select all that apply

Static content such as style sheets, JavaScript files

User-generated videos

E-commerce assets such as product photos

Dynamic content, as determined at request time (cache-behavior configured to forward all headers)

Proxy methods PUT/POST/PATCH/OPTIONS/DELETE go directly to the origin

Question 54

A big data analytics company leverages its proprietary analytics workflow (built using Redshift) to correlate traffic with marketing campaigns and to help retailers optimize hours for peak traffic, among other activities. The company has hired you as an AWS Certified Solutions Architect Professional to review the company's Redshift cluster, which has now become an integral part of its technology solutions. You have been asked to improve the reliability and availability of the cluster in case of a disaster and provide options to ensure that if an issue arises, the cluster can either operate or be restored within five hours.

Which of the following would you suggest as the BEST solution to meet the business needs in the most cost-effective way?

Set up a CloudFormation stack set for Redshift cluster creation so it can be launched in another Region and configure Amazon Redshift to automatically copy snapshots for the cluster to the other AWS Region. In case of a disaster, restore the cluster in the other AWS Region from that Region's snapshot

Configure the Amazon Redshift cluster to make use of Auto Scaling groups with the nodes in the cluster spread across multiple Availability Zones (AZs). In case of a disaster, the nodes in the other AZs will ensure reliability and availability

Set up two identical Amazon Redshift clusters in different regions in a primary-secondary configuration. Develop a solution using the Kinesis Data Streams to collect the data prior to ingestion into the primary Redshift cluster and stream the data to the secondary cluster

Set up two identical Amazon Redshift clusters in different regions in a primary-secondary configuration. Create a cron job to run the UNLOAD command every five hours to export data for all tables in primary cluster to S3. Use cross-region replication from the primary region to secondary region. Create another cron job to ingest the data for all tables from S3 into the secondary cluster using the LOAD command

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Correct AnswerA

Correct option:

"Set up a CloudFormation stack set for Redshift cluster creation so it can be launched in another Region and configure Amazon Redshift to automatically copy snapshots for the cluster to the other AWS Region. In case of a disaster, restore the cluster in the other AWS Region from that Region's snapshot"

A CloudFormation stack set lets you create stacks in AWS accounts across regions by using a single CloudFormation template. All the resources included in each stack are defined by the stack set's CloudFormation template. As you create the stack set, you specify the template to use, as well as any parameters and capabilities that the template requires. For the given use-case, you can set up a CloudFormation stack set for Redshift cluster creation in another Region.

When automated snapshots are enabled for a cluster, Amazon Redshift periodically takes snapshots of that cluster. By default, Amazon Redshift takes a snapshot about every eight hours or following every 5 GB per node of data changes, or whichever comes first. For the given use-case, you can configure Amazon Redshift to automatically copy snapshots for the cluster to another AWS Region. When a snapshot is created in the cluster's primary AWS Region, it's copied to a secondary AWS Region. The two AWS Regions are known respectively as the source AWS Region and destination AWS Region.

In case of a disaster, you can restore your cluster from the snapshot in the destination Region. Amazon Redshift uses the cluster information to create a new cluster. Then it restores all the databases from the snapshot data. The cluster is restored in the destination AWS Region and a random, system-chosen Availability Zone, unless you specify another Availability Zone in your request.

https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-snapshots.html

Incorrect options:

"Set up two identical Amazon Redshift clusters in different regions in a primary-secondary configuration. Develop a solution using the Kinesis Data Streams to collect the data prior to ingestion into the primary Redshift cluster and stream the data to the secondary cluster" - This option is not cost-effective as you need to keep two Redshift clusters in operation for each AWS Region. In addition, Kinesis Data Streams is meant to be used for streaming use-cases and it also adds another layer of costs to this proposed solution. To load or unload data in Redshift, you would rather use the LOAD and UNLOAD command and use S3 as the underlying data repository.

"Set up two identical Amazon Redshift clusters in different regions in a primary-secondary configuration. Create a cron job to run the UNLOAD command every five hours to export data for all tables in primary cluster to S3. Use cross-region replication from the primary region to secondary region. Create another cron job to ingest the data for all tables from S3 into the secondary cluster using the LOAD command" - This option is not cost effective as you need to keep two Redshift clusters in operation for each AWS Region. Moreover, using the LOAD and UNLOAD commands in combination with leveraging cross-region replication for S3 makes this solution complex and difficult to maintain.

"Configure the Amazon Redshift cluster to make use of Auto Scaling groups with the nodes in the cluster spread across multiple Availability Zones (AZs). In case of a disaster, the nodes in the other AZs will ensure reliability and availability" - This option has been added as a distractor as you cannot configure a Redshift cluster to make use of Auto Scaling groups.

References:

https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-snapshots.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/stacksets-concepts.html

Question 55

A medical technology company has recently set up a hybrid cloud between its on-premises data centers and AWS Cloud. The engineering team at the company has developed a Media Archiving and Communication System application that runs on AWS to support real-time collaboration among radiologists and other specialists. The company uses Amazon S3 to aggregate the raw medical images and video footage from its research teams across the world to discover tremendous medical insights. The technical teams at the overseas research facilities have reported huge delays in uploading large video files to the destination S3 bucket.

As a Solutions Architect Professional, which of the following would you recommend as the MOST cost-effective solutions to improve the file upload speed into S3? (Select two)

Select all that apply

Create multiple site-to-site VPN connections between the AWS Cloud and research facilities running in the on-premises data centers. Use these VPN connections for faster file uploads into S3

Use AWS Global Accelerator for faster file uploads into the destination S3 bucket

Create multiple AWS direct connect connections between the AWS Cloud and research facilities running in the on-premises data centers. Use the direct connect connections for faster file uploads into S3

Use Amazon S3 Transfer Acceleration to enable faster file uploads into the destination S3 bucket

Use multipart uploads for faster file uploads into the destination S3 bucket

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Correct AnswersD, E

Correct options:

Use Amazon S3 Transfer Acceleration to enable faster file uploads into the destination S3 bucket - Amazon S3 Transfer Acceleration enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket. Transfer Acceleration takes advantage of Amazon CloudFront’s globally distributed edge locations. As the data arrives at an edge location, data is routed to Amazon S3 over an optimized network path.

https://docs.aws.amazon.com/AmazonS3/latest/dev/transfer-acceleration.html

Use multipart uploads for faster file uploads into the destination S3 bucket - Multipart upload allows you to upload a single object as a set of parts. Each part is a contiguous portion of the object's data. You can upload these object parts independently and in any order. If transmission of any part fails, you can retransmit that part without affecting other parts. After all parts of your object are uploaded, Amazon S3 assembles these parts and creates the object. In general, when your object size reaches 100 MB, you should consider using multipart uploads instead of uploading the object in a single operation. Multipart upload provides improved throughput, therefore it facilitates faster file uploads.

https://docs.aws.amazon.com/AmazonS3/latest/dev/mpuoverview.html

Incorrect options:

Create multiple AWS direct connect connections between the AWS Cloud and research facilities running in the on-premises data centers. Use the direct connect connections for faster file uploads into S3 - AWS Direct Connect is a cloud service solution that makes it easy to establish a dedicated network connection from your premises to AWS. AWS Direct Connect lets you establish a dedicated network connection between your network and one of the AWS Direct Connect locations. Direct connect takes significant time (several months) to be provisioned and is an overkill for the given use-case.

Create multiple site-to-site VPN connections between the AWS Cloud and research facilities running in the on-premises data centers. Use these VPN connections for faster file uploads into S3 - AWS Site-to-Site VPN enables you to securely connect your on-premises network or branch office site to your Amazon Virtual Private Cloud (Amazon VPC). You can securely extend your data center or branch office network to the cloud with an AWS Site-to-Site VPN connection. A VPC VPN Connection utilizes IPSec to establish encrypted network connectivity between your intranet and Amazon VPC over the Internet. VPN Connections are a good solution if you have low to modest bandwidth requirements and can tolerate the inherent variability in Internet-based connectivity. Site-to-site VPN will not help in accelerating the file transfer speeds into S3 for the given use-case.

Use AWS Global Accelerator for faster file uploads into the destination S3 bucket - AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in a single or multiple AWS Regions, such as your Application Load Balancers, Network Load Balancers or Amazon EC2 instances. AWS Global Accelerator will not help in accelerating the file transfer speeds into S3 for the given use-case.

References:

https://docs.aws.amazon.com/AmazonS3/latest/dev/transfer-acceleration.html

https://docs.aws.amazon.com/AmazonS3/latest/dev/mpuoverview.html

Question 56

The engineering team at a retail company has deployed a fleet of EC2 instances under an Auto Scaling group (ASG). The instances under the ASG span two Availability Zones (AZ) within the eu-west-1 region. All the incoming requests are handled by an Application Load Balancer (ALB) that routes the requests to the EC2 instances under the ASG. A planned migration went wrong last week when two instances (belonging to AZ 1) were manually terminated and desired capacity was reduced causing the Availability Zones to become unbalanced. Later that day, another instance (belonging to AZ 2) was detected as unhealthy by the Application Load Balancer's health check.

Which of the following options represent the correct outcomes for the aforesaid events? (Select two)

Select all that apply

As the Availability Zones got unbalanced, Amazon EC2 Auto Scaling will compensate by rebalancing the Availability Zones. When rebalancing, Amazon EC2 Auto Scaling launches new instances before terminating the old ones, so that rebalancing does not compromise the performance or availability of your application

Amazon EC2 Auto Scaling creates a new scaling activity to terminate the unhealthy instance and launch the new instance simultaneously

Amazon EC2 Auto Scaling creates a new scaling activity for terminating the unhealthy instance and then terminates it. Later, another scaling activity launches a new instance to replace the terminated instance

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Correct AnswersC, E

Correct options:

Amazon EC2 Auto Scaling helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You create collections of EC2 instances, called Auto Scaling groups. You can specify the minimum number of instances in each Auto Scaling group, and Amazon EC2 Auto Scaling ensures that your group never goes below this size.

Actions such as changing the Availability Zones for your group or explicitly terminating or detaching instances and decrement the desired capacity at the same time, it can lead to the Auto Scaling group becoming unbalanced between Availability Zones. Amazon EC2 Auto Scaling compensates by rebalancing the Availability Zones.

When rebalancing, Amazon EC2 Auto Scaling launches new instances before terminating the old ones, so that rebalancing does not compromise the performance or availability of your application. Therefore, this option is correct.

Availability Zone Rebalancing Overview: https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html

However, the scaling activity of Auto Scaling works in a different sequence compared to the rebalancing activity. Auto Scaling creates a new scaling activity for terminating the unhealthy instance and then terminates it. Later, another scaling activity launches a new instance to replace the terminated instance.

Incorrect options:

Amazon EC2 Auto Scaling creates a new scaling activity for launching a new instance to replace the unhealthy instance. Later, EC2 Auto Scaling creates a new scaling activity for terminating the unhealthy instance and then terminates it - This option contradicts the correct sequence of events outlined earlier for scaling activity created by EC2 Auto Scaling. Actually, Auto Scaling first terminates the unhealthy instance and then launches a new instance. Hence this is incorrect.

As the Availability Zones got unbalanced, Amazon EC2 Auto Scaling will compensate by rebalancing the Availability Zones. When rebalancing, Amazon EC2 Auto Scaling terminates old instances before launching new instances, so that rebalancing does not cause extra instances to be launched - This option contradicts the correct sequence of events outlined earlier for rebalancing activity. When rebalancing, Amazon EC2 Auto Scaling launches new instances before terminating the old ones. Hence this is incorrect.

Amazon EC2 Auto Scaling creates a new scaling activity to terminate the unhealthy instance and launch the new instance simultaneously - This is a made-up option as both the terminate and launch activities can't happen simultaneously. This option has been added as a distractor.

References:

https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-instance-termination.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/healthcheck.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/common-scenarios-termination.html#common-scenarios-termination-rebalancing

Question 57

A social media company has a serverless application stack that consists of CloudFront, API Gateway and Lambda functions. The company has hired you as an AWS Certified Solutions Architect Professional to improve the current deployment process which creates a new version of the Lambda function and then runs an AWS CLI script for deployment. In case the new version errors out, then another CLI script is invoked to deploy the previous working version of the Lambda function. The company has mandated you to decrease the time to deploy new versions of the Lambda functions and also reduce the time to detect and rollback when errors are identified.

Which of the following solutions would you suggest for the given use-case?

Use Serverless Application Model (SAM) and leverage the built-in traffic-shifting feature of SAM to deploy the new Lambda version via CodeDeploy and use pre-traffic and post-traffic test functions to verify code. Rollback in case CloudWatch alarms are triggered

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Correct AnswerB

Correct option:

The AWS Serverless Application Model (SAM) is an open source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. You define the application you want with just a few lines per resource and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax. Then, CloudFormation provisions your resources with reliable deployment capabilities.

To address the given use-case, you can use the traffic shifting feature of SAM to easily test the new version of the Lambda function without having to manually move 100% of the traffic to the new version in one shot.

You can use CodeDeploy to create a deployment process that publishes the new Lambda version but does not send any traffic to it. Then it executes a PreTraffic test to ensure that your new function works as expected. After the test succeeds, CodeDeploy automatically shifts traffic gradually to the new version of the Lambda function. This workflow address one of the key requirements of reducing the time to detect errors. You can roll back to the previous version in case the new version errors out.

https://aws.amazon.com/blogs/compute/implementing-safe-aws-lambda-deployments-with-aws-codedeploy/

Incorrect options:

Set up and deploy nested CloudFormation stacks with the CloudFront distribution as well as the API Gateway in the parent stack. Create and deploy a child stack containing the Lambda functions. To address any changes in a Lambda function, create a CloudFormation change set and deploy. In case the Lambda function errors out, rollback the CloudFormation change set to the previous version - You can use CloudFormation change sets to preview how proposed changes to a stack might impact your running resources, for example, whether your changes will delete or replace any critical resources, AWS CloudFormation makes the changes to your stack only when you decide to execute the change set, allowing you to decide whether to proceed with your proposed changes or explore other changes by creating another change set.

This option does not help in reducing the time to detect any potential deployment errors as you would not know about any potential failures until you actually deploy the stack.

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-updating-stacks-changesets.html

Instead, you should use SAM to create your serverless application as it comes built-in with CodeDeploy to provide gradual Lambda deployments. Also, you can define pre-traffic and post-traffic test functions to verify that the newly deployed code is configured correctly and your application operates as expected. You can roll back the deployment if CloudWatch alarms are triggered.

Set up and deploy a CloudFormation stack containing a new API Gateway endpoint that points to the new Lambda version. Test the updated CloudFront origin that points to this new API Gateway endpoint and in case errors are detected then revert the CloudFront origin to the previous working API Gateway endpoint - This option does not help in reducing the time to detect any potential deployment errors as you would not know about any potential failures until you actually deploy the stack and point to the new endpoint.

Set up and deploy nested CloudFormation stacks with the CloudFront distribution as well as the API Gateway in the parent stack. Create and deploy a child stack containing the Lambda functions. To address any changes in a Lambda function, create a CloudFormation change set and deploy. Use pre-traffic and post-traffic test functions of the change set to verify the deployment. Rollback in case CloudWatch alarms are triggered - This option has been added as a distractor, since CloudFormation change sets do not have pre-traffic and post-traffic test functions. Therefore this option is incorrect.

References:

https://aws.amazon.com/blogs/compute/implementing-safe-aws-lambda-deployments-with-aws-codedeploy/

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-updating-stacks-changesets.html

https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/automating-updates-to-serverless-apps.html

Question 58

A silicon valley based unicorn startup recently launched a video-sharing social networking service called KitKot. The startup uses AWS Cloud to manage the IT infrastructure. Users upload video files up to 1 GB in size to a single EC2 instance based application server which stores them on a shared EFS file system. Another set of EC2 instances managed via an Auto Scaling group, periodically scans the EFS share directory for new files to process and generate new videos (for thumbnails and composite visual effects) according to the video processing instructions that are uploaded alongside the raw video files. Post-processing, the raw video files are deleted from the EFS file system and the results are stored in an S3 bucket. Links to the processed video files are sent via in-app notifications to the users. The startup has recently found that even as more instances are added to the Auto Scaling Group, many files are processed twice, therefore image processing speed is not improved.

As an AWS Certified Solutions Architect Professional, what would you recommend to improve the reliability of the solution as well as eliminate the redundant processing of video files?

Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Configure an S3 trigger to invoke a Lambda function on each video file upload to S3 that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from the SQS queue and the S3 bucket. Configure the queue depth metric to scale the size of the Auto Scaling group for video processing instances. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files

Refactor the application to run from Amazon S3 instead of the EFS file system and upload the video files directly to an S3 bucket via an API Gateway based REST API. Configure an S3 trigger to invoke a Lambda function each time a file is uploaded and the Lambda in turn processes the video and stores the processed files in another bucket. Leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files

Refactor the application to run from S3 instead of EFS and upload the video files directly to an S3 bucket. Set CloudWatch Events to trigger a Lambda function on each file upload that puts a message in an SQS queue containing the link and the video processing instructions. Change the video processing application to read from SQS queue for new files and configure the queue depth metric to scale instances in the video processing Auto Scaling group. Leverage CloudWatch Events to trigger an SNS notification to the user containing the links to the processed files

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Correct AnswerA

Correct option:

For the given use-case, the primary way to address the issues related to reliability, as well as redundant processing of video files, is by introducing SQS into the solution stack. SQS offers a secure, durable, and available hosted queue that lets you integrate and decouple distributed software systems and components. SQS locks your messages during processing, so that multiple producers can send and multiple consumers can receive messages at the same time. Using the right combination of delay queues and visibility timeout, you can optimize the solution to address use-cases where the consumer application needs additional time to process messages such as the one in this scenario. Messages are put into the SQS queue via a Lambda function that is triggered when a new video file is uploaded to S3 for processing.

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-delay-queues.html

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

To ensure that the consumer applications running on the video processing instances can scale via an Auto Scaling group, you could use the SQS queue depth (known as the CloudWatch Amazon SQS metric - ApproximateNumberOfMessages) as the underlying metric. However, the issue with using a CloudWatch Amazon SQS metric like ApproximateNumberOfMessagesVisible for target tracking is that the number of messages in the queue might not change proportionally to the size of the Auto Scaling group that processes messages from the queue. An optimized solution would be to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html

Incorrect options:

Refactor the application to run from Amazon S3 instead of the EFS file system and upload the video files directly to an S3 bucket via an API Gateway based REST API. Configure an S3 trigger to invoke a Lambda function each time a file is uploaded and the Lambda, in turn, processes the video and stores the processed files in another bucket. Leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files - API Gateway supports payload size of only up to 10 MB therefore this option is incorrect for the given use-case since you need to support file sizes of up to 1GB for video processing.

https://docs.aws.amazon.com/apigateway/latest/developerguide/limits.html

S3 supports only the following destinations where it can publish events - SNS, SQS and Lambda. Therefore this option is incorrect.

Create an hourly cron job on the application server to synchronize the contents of the EFS share with S3. Trigger a Lambda function every time a file is uploaded to S3 and process the video file to store the results in another S3 bucket. Once the file is processed, leverage CloudWatch Events to trigger an SNS notification to send an in-app notification to the user containing the links to the processed files - The issue with this option is lack of reliability. In case the Lambda function (which is triggered when a video file is uploaded to S3) fails to process a given video file, then the source video file would always remain unprocessed as there is no queue-based mechanism to re-process failed events. So this option is incorrect.

References:

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-delay-queues.html

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html

https://docs.aws.amazon.com/apigateway/latest/developerguide/limits.html

Question 59

A leading hotel reviews website has a repository of more than one million high-quality digital images. When this massive volume of images became too cumbersome to handle in-house, the company decided to offload the content to a central repository on Amazon S3 as part of its hybrid cloud strategy. The company now wants to reprocess its entire collection of photographic images to change the watermarks. The company wants to use Amazon EC2 instances and Amazon SQS in an integrated workflow to generate the sizes they need for each photo. The team wants to process a few thousand photos each night, using Amazon EC2 Spot Instances. The team uses Amazon SQS to communicate the photos that need to be processed and the status of the jobs. To handle certain sensitive photos, the team wants to postpone the delivery of certain messages to the queue by one minute while all other messages need to be delivered immediately to the queue.

As a Solutions Architect Professional, which of the following solutions would you suggest to the company to handle the workflow for sensitive photos?

Use visibility timeout to postpone the delivery of certain messages to the queue by one minute

Use dead-letter queues to postpone the delivery of certain messages to the queue by one minute

Use delay queues to postpone the delivery of certain messages to the queue by one minute

Use message timers to postpone the delivery of certain messages to the queue by one minute

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Correct AnswerD

Correct option:

Use message timers to postpone the delivery of certain messages to the queue by one minute

You can use message timers to set an initial invisibility period for a message added to a queue. So, if you send a message with a 60-second timer, the message isn't visible to consumers for its first 60 seconds in the queue. The default (minimum) delay for a message is 0 seconds. The maximum is 15 minutes. Therefore, you should use message timers to postpone the delivery of certain messages to the queue by one minute.

Incorrect options:

Use dead-letter queues to postpone the delivery of certain messages to the queue by one minute - Dead-letter queues can be used by other queues (source queues) as a target for messages that can't be processed (consumed) successfully. Dead-letter queues are useful for debugging your application or messaging system because they let you isolate problematic messages to determine why their processing doesn't succeed. You cannot use dead-letter queues to postpone the delivery of certain messages to the queue by one minute.

Use visibility timeout to postpone the delivery of certain messages to the queue by one minute - Visibility timeout is a period during which Amazon SQS prevents other consumers from receiving and processing a given message. The default visibility timeout for a message is 30 seconds. The minimum is 0 seconds. The maximum is 12 hours. You cannot use visibility timeout to postpone the delivery of certain messages to the queue by one minute.

Use delay queues to postpone the delivery of certain messages to the queue by one minute - Delay queues let you postpone the delivery of all new messages to a queue for several seconds, for example, when your consumer application needs additional time to process messages. If you create a delay queue, any messages that you send to the queue remain invisible to consumers for the duration of the delay period. The default (minimum) delay for a queue is 0 seconds. The maximum is 15 minutes. You cannot use delay queues to postpone the delivery of only certain messages to the queue by one minute.

References:

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-message-timers.html

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-delay-queues.html

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

Question 60

A gaming company runs its flagship application with an SLA of 99.99%. Global users access the application 24/7. The application is currently hosted on the on-premises data centers and it routinely fails to meet its SLA, especially when hundreds of thousands of users access the application concurrently. The engineering team has also received complaints from some users about high latency.

As a Solutions Architect Professional, how would you redesign this application for scalability and also allow for automatic failover at the lowest possible cost?

Configure Route 53 geolocation-based routing to route to the nearest Region and activate the health checks. Host the website behind a Network Load Balancer (NLB) with targets as ECS containers using Fargate. Repeat this configuration of NLB with ECS containers using Fargate in multiple Regions. Use Aurora Global database as the data layer

Configure Route 53 latency-based routing to route to the nearest Region and activate the health checks. Host the website on S3 in each Region and use API Gateway with AWS Lambda for the application layer. Set up the data layer using DynamoDB global tables with DAX for caching

Configure Route 53 round-robin routing policy to distribute load evenly across all Regions and activate the health checks. Host the website behind a Network Load Balancer (NLB) with targets as ECS containers using Fargate. Repeat this configuration of NLB with ECS containers using Fargate in multiple Regions. Use Aurora Global database as the data layer

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Correct AnswerC

Correct option:

You can use Route 53 routing policies for a hosted zone record to determine how Amazon Route 53 responds to queries.

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html

You can use a combination of alias records (such as latency alias, weighted alias or failover alias) and non-alias records to build a decision tree that gives you greater control over how Route 53 responds to requests. For example, you might use latency alias records to select a Region close to a user and use weighted records for two or more resources within each Region to protect against the failure of a single endpoint or an Availability Zone.

The following schematic shows how you can use a combination of latency routing policy (that has health checks activated) with a weighted routing policy to manage a multi-Region routing infrastructure. In case a Region goes down, Route 53 would look for the latency alias record with the next-best latency and choose the record for the other Region.

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/dns-failover-complex-configs.html

Therefore, for the given use-case, this solution allows for automatic failover while minimizing the latency. As the downstream components for this solution such as the API Gateway, Lambda functions and DynamoDB global tables (with DAX) are serverless, so it allows the solution to scale easily.

Incorrect options:

Geolocation routing is used when you want to route traffic based on the location of your users. When you use geolocation routing, you can localize your content and present some or all of your website in the language of your users. You can also use geolocation routing to restrict the distribution of content to only the locations in which you have distribution rights. Other components mentioned in the stack are not a deal-breaker, but using geolocation-based routing makes this option incorrect.

Configure a combination of Route 53 failover routing with geolocation-based routing. Host the website behind an Application Load Balancer (ALB) with targets as EC2 instances that are automatically scaled via Auto-Scaling Group (ASG). Repeat this configuration of ALB with EC2 instances as targets that are scaled via ASG in multiple Regions. Use a Multi-AZ deployment with RDS MySQL as the data layer - As mentioned earlier, you cannot use geolocation-based routing to route to the nearest Region as you need to use latency based routing to accomplish that. Failover routing could help with ensuring failover across AWS Regions, but using geolocation-based routing is a deal-breaker. Another red-flag is using Multi-AZ RDS MySQL as data layer, which can be deployed in just one AWS Region. You would need to develop and maintain custom data sync scripts/jobs to maintain data consistency across Regions.

References:

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/dns-failover-complex-configs.html

https://aws.amazon.com/premiumsupport/knowledge-center/route-53-dns-health-checks/

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