SAP-C02 Amazon Web Services AWS Certified Solutions Architect

AWS Certified Solutions Architect - Professional

Last Update 14 hours ago Total Questions : 674

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Question # 106

An online gaming company needs to optimize the cost of its workloads on AWS. The company uses a dedicated account to host the production environment for its online gaming application and an analytics application.

Amazon EC2 instances host the gaming application and must always be vailable. The EC2 instances run all year. The analytics application uses data that is stored in Amazon S3. The analytics application can be interrupted and resumed without issue.

Which solution will meet these requirements MOST cost-effectively?

Purchase an EC2 Instance Savings Plan for the online gaming application instances. Use On-Demand Instances for the analytics application.

Purchase an EC2 Instance Savings Plan for the online gaming application instances. Use Spot Instances for the analytics application.

Use Spot Instances for the online gaming application and the analytics application. Set up a catalog in AWS Service Catalog to provision services at a discount.

Use On-Demand Instances for the online gaming application. Use Spot Instances for the analytics application. Set up a catalog in AWS Service Catalog to provision services at a discount.

Answer:

Explanation:

The correct answer is B.

B. This solution is the most cost-effective because it uses an EC2 Instance Savings Plan for the online gaming application instances, which provides the lowest prices and savings up to 72% compared to On-Demand prices. The EC2 Instance Savings Plan applies to any instance size within the same family and region, regardless of availability zone, operating system, or tenancy. The online gaming application instances run all year and must always be available, so they are not suitable for Spot Instances, which can be interrupted with a two-minute notice. This solution also uses Spot Instances for the analytics application, which can reduce the cost by up to 90% compared to On-Demand prices. The analytics application can be interrupted and resumed without issue, so it is a good fit for Spot Instances, which use spare EC2 capacity.This solution does not require AWSService Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts123

A. This solution is incorrect because it uses On-Demand Instances for the analytics application, which are more expensive than Spot Instances. The analytics application can be interrupted and resumed without issue, so it can benefit from the lower cost of Spot Instances, which use spare EC2 capacity.

C. This solution is incorrect because it uses Spot Instances for the online gaming application, which can be interrupted with a two-minute notice. The online gaming application instances must always be available, so they are not suitable for Spot Instances, which use spare EC2 capacity. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

D. This solution is incorrect because it uses On-Demand Instances for the online gaming application, which are more expensive than an EC2 Instance Savings Plan. The online gaming application instances run all year and must always be available, so they are suitable for an EC2 Instance Savings Plan, which provides the lowest prices and savings up to 72% compared to On-Demand prices. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

[References:, 1:EC2 Instance Savings Plans – Amazon Web Services2:Amazon EC2 Spot Instances3:Cloud Management and Governance – AWS Service Catalog – Amazon Web Services, , , , , , , , , , ]

Question # 107

A company generates approximately 20 GB of data multiple times each day. The company uses AWS DataSync to copy all data from on-premises storage to Amazon S3 every 6 hours for further processing.

The analytics team wants to modify the copy process to copy only data relevant to the analytics team and ignore the rest of the data. The team wants to copy data as soon as possible and receive a notification when the copy process is finished.

Which combination of steps will meet these requirements MOST cost-effectively? (Select THREE.)

Modify the data generation process on premises to create a manifest file at the end of the copy process with the names of the objects to be copied to Amazon S3. Create a custom script to upload the manifest file to an S3 bucket.

Modify the data generation process on premises to create a manifest file at the end of the copy process with the names of the objects to be copied to Amazon S3. Create an AWS Lambda function to load the manifest file data into an Amazon DynamoDB table.

Create an AWS Lambda function that Amazon EventBridge invokes when the manifest file is loaded into Amazon DynamoDB. Configure the Lambda function to copy the data from on-premises storage to the S3 bucket that uses the manifest file.

Create an AWS Lambda function that an S3 Event Notification invokes when the manifest file is uploaded. Configure the Lambda function to invoke the DataSync task by calling the StartTaskExecution API action with a manifest.

Create an Amazon SNS topic. Create an Amazon EventBridge rule to send an email notification to the SNS topic when the DataSync task execution status changes to SUCCESS or to ERROR.

Create an Amazon SNS topic. Create an AWS Lambda function to send an email notification to the SNS topic when the DataSync task execution status changes to SUCCESS or to ERROR.

Answer:

A, D, E

Explanation:

The analytics team wants to copy only a subset of the data, run the copy as soon as data is ready, and receive notifications when the process completes. AWS DataSync supports the use of manifest files to control exactly which files or objects are transferred. By generating a manifest file on premises that lists only the relevant data and uploading that manifest to Amazon S3, the company can precisely limit what DataSync copies, which reduces data transfer costs and processing overhead.

Option A satisfies the requirement to identify only analytics-relevant data by creating and uploading a manifest file that lists the objects to be transferred. This avoids copying unnecessary data and is cost-effective.

Option D uses Amazon S3 Event Notifications to trigger an AWS Lambda function as soon as the manifest file is uploaded. The Lambda function starts the DataSync task and references the manifest file. This ensures that the copy process begins immediately after data generation, rather than waiting for a fixed schedule, which meets the “as soon as possible” requirement.

Option E provides a fully managed and scalable notification mechanism. AWS DataSync publishes task execution state changes as events. Amazon EventBridge can capture SUCCESS or ERROR states and route those events to Amazon SNS, which can then send email notifications. This approach avoids custom polling logic and minimizes operational overhead.

Option B and C introduce Amazon DynamoDB unnecessarily. DataSync can directly consume a manifest file from Amazon S3, so loading manifest data into DynamoDB and orchestrating copy logic manually adds cost and complexity without benefit.

Option F relies on a custom Lambda function to monitor task state changes, which increases operational overhead compared to the native EventBridge integration with DataSync.

Therefore, using S3-based manifests, event-driven invocation of DataSync, and EventBridge-based notifications is the most cost-effective and operationally efficient solution.

[References:AWS documentation on AWS DataSync manifest files for selective data transfer.AWS documentation on integrating AWS DataSync with Amazon S3 Event Notifications and AWS Lambda.AWS documentation on AWS DataSync task state change events and Amazon EventBridge integration with Amazon SNS for notifications., , , ]

Question # 108

A company has developed a mobile game. The backend for the game runs on several virtual machines located in an on-premises data center. The business logic is exposed using a REST API with multiple functions. Player session data is stored in central file storage. Backend services use different API keys for throttling and to distinguish between live and test traffic.

The load on the game backend varies throughout the day. During peak hours, the server capacity is not sufficient. There are also latency issues when fetching player session data. Management has asked a solutions architect to present a cloud architecture that can handle the game ' s varying load and provide low-latency data access. The API model should not be changed.

Which solution meets these requirements?

Implement the REST API using a Network Load Balancer (NLB). Run the business logic on an Amazon EC2 instance behind the NLB. Store player session data in Amazon Aurora Serverless.

Implement the REST API using an Application Load Balancer (ALB). Run the business logic in AWS Lambda. Store player session data in Amazon DynamoDB with on-demand capacity.

Implement the REST API using Amazon API Gateway. Run the business logic in AWS Lambda. Store player session data in Amazon DynamoDB with on- demand capacity.

Implement the REST API using AWS AppSync. Run the business logic in AWS Lambda. Store player session data in Amazon Aurora Serverless.

Question # 109

An external audit of a company ' s serverless application reveals IAM policies that grant too many permissions. These policies are attached to the company ' s AWS Lambda execution roles. Hundreds of the company ' s Lambda functions have broad access permissions, such as full access to Amazon S3 buckets and Amazon DynamoDB tables. The company wants each function to have only the minimum permissions that the function needs to complete its task.

A solutions architect must determine which permissions each Lambda function needs.

What should the solutions architect do to meet this requirement with the LEAST amount of effort?

Set up Amazon CodeGuru to profile the Lambda functions and search for AWS API calls. Create an inventory of the required API calls and resources for each Lambda function. Create new IAM access policies for each Lambda function. Review the new policies to ensure that they meet the company ' s business requirements.

Turn on AWS CloudTrail logging for the AWS account. Use AWS Identity and Access Management Access Analyzer to generate IAM access policies based on the activity recorded in the CloudTrail log. Review the generated policies to ensure that they meet the company ' s business requirements.

Turn on AWS CloudTrail logging for the AWS account. Create a script to parse the CloudTrail log, search for AWS API calls by Lambda execution role, and create a summary report. Review the report. Create IAM access policies that provide more restrictive permissions for each Lambda function.

Turn on AWS CloudTrail logging for the AWS account. Export the CloudTrail logs to Amazon S3. Use Amazon EMR to process the CloudTrail logs in Amazon S3 and produce a report of API calls and resources used by each execution role. Create a new IAM access policy for each role. Export the generated roles to an S3 bucket. Review the generated policies to ensure that they meet the company ' s business requirements.

Question # 110

A company is serving files to its customers through an SFTP server that is accessible over the internet The SFTP server is running on a single Amazon EC2 instance with an Elastic IP address attached Customers connect to the SFTP server through its Elastic IP address and use SSH for authentication The EC2 instance also has an attached security group that allows access from all customer IP addresses.

A solutions architect must implement a solution to improve availability minimize the complexity of infrastructure management and minimize the disruption to customers who access files. The solution must not change the way customers connect

Which solution will meet these requirements?

Disassociate the Elastic IP address from the EC2 instance Create an Amazon S3 bucket to be used for SFTP file hosting Create an AWS Transfer Family server. Configure the Transfer Family server with a publicly accessible endpoint Associate the SFTP Elastic IP address with the new endpoint. Point the Transfer Family server to the S3 bucket Sync all files from the SFTP server to the S3 bucket.

Disassociate the Elastic IP address from the EC2 instance Create an Amazon S3 bucket to be used for SFTP file hosting Create an AWS Transfer Family Server Configure the Transfer Family server with a VPC-hosted, internet-facing endpoint Associate the SFTP Elastic IP address with the new endpoint Attach the security group with customer IP addresses to the new endpoint Point the Transfer Family server to the S3 bucket. Sync all files from the

Disassociate the Elastic IP address from the EC2 instance. Create a new Amazon Elastic File System (Amazon EFS) file system to be used for SFTP file hosting. Create an AWS Fargate task definition to run an SFTP server Specify the EFS file system as a mount in the task definition Create a Fargate service by using the task definition, and place a Network Load Balancer (NLB) in front of the service. When configuring the service, attach the sec

Disassociate the Elastic IP address from the EC2 instance. Create a multi-attach Amazon Elastic Block Store (Amazon EBS) volume to be used for SFTP file hosting. Create a Network Load Balancer (NLB) with the Elastic IP address attached. Create an Auto Scaling group with EC2 instances that run an SFTP server. Define in the Auto Scaling group that instances that are launched should attach the new multi-attach EBS volume Configure the Auto Sca

Question # 111

A company runs a web application on a single Amazon EC2 instance. End users experience slow application performance during times of peak usage, when CPU utilization is consistently more than 95%.

A user data script installs required custom packages on the EC2 instance. The process of launchingthe instance takes several minutes.

The company is creating an Auto Scaling group that has mixed instance groups, varied CPUs, and a maximum capacity limit. The Auto Scaling group will use a launch template for various configuration options. The company needs to decrease application latency when new instances are launched during auto scaling.

Which solution will meet these requirements?

Use a predictive scaling policy. Use an instance maintenance policy to run the user data script. Set the default instance warmup time to 0 seconds.

Use a dynamic scaling policy. Use lifecycle hooks to run the user data script. Set the default instance warmup time to 0 seconds.

Use a predictive scaling policy. Enable warm pools for the Auto Scaling group. Use an instance maintenance policy to run the user data script.

Use a dynamic scaling policy. Enable warm pools for the Auto Scaling group. Use lifecycle hooks to run the user data script.

Question # 112

A company collects air quality data from sensors. The company plans to use the MQTT protocol to send the data to AWS IoT Core. The company will process the data and then will store the data in an Amazon Aurora database.

During periods of low air quality, sensors will send data more frequently. The company must buffer the data during these periods to make sure that no data is lost before the data is processed and stored.

Which solution will meet these requirements?

Create an Amazon Kinesis data stream. Create an AWS IoT rule action and set the data stream as the target. Create an AWS Step Functions state machine that is invoked by the data stream. Use the state machine to process and store the data.

Create an Amazon Kinesis data stream. Create an AWS IoT rule action and set the data stream as the target. Create an application that runs on an Amazon ECS cluster with the AWS Fargate launch type. Configure the application to read data from the data stream, process the data, and store the data.

Create an Amazon SQS queue. Create an AWS IoT rule action and set the SQS queue as the target. Create an AWS Step Functions state machine that is invoked by the SQS queue. Use the state machine to process and store the data.

Create an Amazon SNS topic. Create an AWS IoT rule action and set the SNS topic as the target. Create an application that runs on an Amazon ECS cluster with the AWS Fargate launch type. Configure the application to read data from the SNS topic, process the data, and store the data.

Question # 113

A company is developing a gene reporting device that will collect genomic information to assist researchers with collecting large samples of data from a diverse population. The device will push 8 KB of genomic data every second to a data platform that will need to process and analyze the data and provide information back to researchers. The data platform must meet the following requirements:

•Provide near-real-time analytics of the inbound genomic data

•Ensure the data is flexible, parallel, and durable

•Deliver results of processing to a data warehouse

Which strategy should a solutions architect use to meet these requirements?

Use Amazon Kinesis Data Firehose to collect the inbound sensor data, analyze the data with Kinesis clients, and save the results to an Amazon RDS instance.

Use Amazon Kinesis Data Streams to collect the inbound sensor data, analyze the data with Kinesis clients, and save the results to an Amazon Redshift cluster using Amazon EMR.

Use Amazon S3 to collect the inbound device data, analyze the data from Amazon SOS with Kinesis, and save the results to an Amazon Redshift cluster.

Use an Amazon API Gateway to put requests into an Amazon SQS queue, analyze the data with an AWS Lambda function, and save the results to an Amazon Redshift cluster using Amazon EMR.

Question # 114

A company that provisions job boards for a seasonal workforce is seeing an increase in traffic and usage. The backend services run on a pair of Amazon EC2 instances behind an Application Load Balancer with Amazon DynamoDB as the datastore. Application read and write traffic is slow during peak seasons.

Which option provides a scalable application architecture to handle peak seasons with the LEAST development effort?

Migrate the backend services to AWS Lambda. Increase the read and write capacity of DynamoDB.

Migrate the backend services to AWS Lambda. Configure DynamoDB to use global tables.

Use Auto Scaling groups for the backend services. Use DynamoDB auto scaling.

Use Auto Scaling groups for the backend services. Use Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB.

Answer:

Explanation:

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

Question # 115

A company is using an organization in AWS organization to manage AWS accounts. For each new project the company creates a new linked account. After the creation of a new account, the root user signs in to the new account and creates a service request to increase the service quota for Amazon EC2 instances. A solutions architect needs to automate this process.

Which solution will meet these requirements with tie LEAST operational overhead?

Create an Amazon EventBridge rule to detect creation of a new account Send the event to an Amazon Simple Notification Service (Amazon SNS) topic that invokes an AWS Lambda function. Configure the Lambda function to run the request-service-quota-increase command to request a service quota increase for EC2 instances.

Create a Service Quotas request template in the management account. Configure the desired service quota increases for EC2 instances.

Create an AWS Config rule in the management account to set the service quota for EC2 instances.

Create an Amazon EventBridge rule to detect creation of a new account. Send the event to an Amazon simple Notification service (Amazon SNS) topic that involves an AWS Lambda function. Configure the Lambda function to run the create-case command to request a service quota increase for EC2 instances.

Question # 116

A company hosts a read-heavy web application on Amazon EKS and Amazon DynamoDB. The company uses Amazon Route 53 as its DNS solution. The application serves hundreds of users during peak usage periods. Most users are in the United States, but the application is experiencing growing usage in Europe and the Middle East. The company needs to reduce application latency for all global users.

Which solution will meet this requirement?

Deploy the application to multiple AWS Regions. Create a global secondary index (GSI) in the primary Region. Increase provisioned throughput for DynamoDB read capacity units (RCUs). Update the application to use the GSI. Create a Route 53 failover record set with the us-east-1 Region as the primary Region. Set the eu-central-1 Region and the me-central-1 Region as secondary Regions. Configure Route 53 health checks and enable Evaluate Targe

Use a DynamoDB Accelerator (DAX) cluster with memory-optimized instances. Deploy the application to three Availability Zones. Update the application to use the DAX endpoints. Update the Route 53 record set to use the new deployment endpoints.

Increase the compute resources for the application. Create an Amazon CloudFront distribution and set the application endpoint as the origin. Create DynamoDB read replicas in the primary AWS Region. Update the application to use the DynamoDB replica endpoints.

Deploy the application to multiple AWS Regions. Convert the DynamoDB table to a global table with replicas in target Regions. Update the application to access the nearest DynamoDB replica. Create a Route 53 geolocation record set for the target Regions. Configure Route 53 health checks and enable Evaluate Target Health.

Answer:

Explanation:

The requirement is to reduce application latency for a globally distributed user base, with users primarily in the United States but increasing usage in Europe and the Middle East. The application is read-heavy and uses Amazon DynamoDB as its data store. Reducing latency for global users requires placing both the application compute and the data closer to users in each Region.

Option D uses the correct multi-Region architecture. Deploying the application to multiple AWS Regions places Amazon EKS workloads closer to end users. Converting the DynamoDB table to a global table creates fully managed, multi-Region replicas of the data with low-latency replication. This allows reads to be served locally from the nearest DynamoDB replica, significantly reducing read latency for users outside the primary Region. Because the workload is read-heavy, this design is particularly effective.

Route 53 geolocation routing directs users to the Region closest to them based on their geographic location. Combined with health checks and Evaluate Target Health, this ensures users are routed to the nearest healthy Region, improving both latency and availability.

Option A is incorrect because global secondary indexes do not replicate data across Regions. GSIs operate within a single Region and are used to support alternative query patterns, not global data distribution. Additionally, Route 53 failover routing is designed for disaster recovery, not for performance optimization or global load balancing.

Option B uses DynamoDB Accelerator (DAX), which improves read latency by caching data in memory, but DAX is Region-specific and does not reduce cross-Region network latency for users in Europe and the Middle East accessing a US-based application. It also does not address application-level latency caused by distance to the EKS cluster.

Option C is not valid because DynamoDB does not support read replicas in the same way relational databases do. DynamoDB replication for latency and global access is achieved through global tables, not through read replicas in a single Region. CloudFront can reduce latency for static or cacheable HTTP content, but it does not solve backend application latency or DynamoDB access latency for dynamic, read-heavy requests.

Therefore, deploying the application and DynamoDB as global tables across Regions and using Route 53 geolocation routing is the correct solution to minimize latency for all global users.

[References:AWS documentation on Amazon DynamoDB global tables for multi-Region, low-latency data access.AWS documentation on Route 53 geolocation routing and health checks for directing users to the closest Region., , ]

Question # 117

A company has a few AWS accounts for development and wants to move its production application to AWS. The company needs to enforce Amazon Elastic Block Store (Amazon EBS) encryption at rest current production accounts and future production accounts only. The company needs a solution that includes built-in blueprints and guardrails.

Which combination of steps will meet these requirements? (Choose three.)

Use AWS CloudFormation StackSets to deploy AWS Config rules on production accounts.

Create a new AWS Control Tower landing zone in an existing developer account. Create OUs for accounts. Add production and development accounts to production and development OUs, respectively.

Create a new AWS Control Tower landing zone in the company’s management account. Addproduction and development accounts to production and development OUs. respectively.

Invite existing accounts to join the organization in AWS Organizations. Create SCPs to ensure compliance.

Create a guardrail from the management account to detect EBS encryption.

Create a guardrail for the production OU to detect EBS encryption.

Question # 118

A company is running an event ticketing platform on AWS and wants to optimize the platform ' s cost-effectiveness. The platform is deployed on Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 and is backed by an Amazon RDS for MySQL DB instance. The company is developing new application features to run on Amazon EKS with AWS Fargate.

The platform experiences infrequent high peaks in demand. The surges in demand depend on event dates.

Which solution will provide the MOST cost-effective setup for the platform?

Purchase Standard Reserved Instances for the EC2 instances that the EKS cluster uses in its baseline load. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet predicted peak load for the year.

Purchase Compute Savings Plans for the predicted medium load of the EKS cluster. Scale the cluster with On-Demand Capacity Reservations based on event dates for peaks. Purchase 1-year No Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale out database read replicas during peaks.

Purchase EC2 Instance Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Purchase Compute Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Question # 119

A company wants to use Amazon S3 to back up its on-premises file storage solution. The company ' s on-premises file storage solution supports NFS, and the company wants its new solution to support NFS. The company wants to archive the backup files after 5 days. If the company needs archived files for disaster recovery, t he company is willing to wait a few days for the retrieval of those files.

Which solution meets these requirements MOST cost-effectively?

Deploy an AWS Storage Gateway files gateway that is associated with an S3 bucket. Move the files from the on-premises file storage solution to the file gateway. Create an S3 Lifecycle rule to move the file to S3 Standard-Infrequent Access (S3 Standard-IA) after 5 days.

Deploy an AWS Storage Gateway volume gateway that is associated with an S3 bucket. Move the files from the on-premises file storage solution to the volume gateway. Create an S3 Lifecycle rule to move the files to S3 Glacier Deep Archive after 5 days.

Deploy an AWS Storage Gateway tape gateway that is associated with an S3 bucket. Move the files from the on-premises file storage solution to the tape gateway. Create an S3 Lifecycle rule to move the files to S3 Standard-Infrequent Access (S3 Standard-IA) after 5 days.

Deploy an AWS Storage Gateway file gateway that is associated with an S3 bucket. Move the files from the on-premises file storage solution to the tape gateway. Create an S3 Lifecycle rule to move the files to S3 Standard-Infrequent Access (S3 Standard-IA) after 5 days.

Deploy an AWS Storage Gateway file gateway that is associated with an S3 bucket. Move the files from the on-premises file storage solution to the file gateway. Create an S3 Lifecycle rule to move the files to S3 Glacier Deep Archive after 5 days.

Question # 120

A company stores all its infrastructure code in a central Git repository that is hosted on GitHub Enterprise. The company wants to implement a process that submits every infrastructure update by using a pull request. Each pull request must automatically receive an AWS CloudFormation change set. After the pull request is approved, CloudFormation must apply the updates directly. The process must not require custom pipelines or manual template processing.

Which solution will meet these requirements?

Use CloudFormation to invoke an AWS CodePipeline action. Configure the CodePipeline action to validate the template and publish the validation results as pull request comments.

Use CloudFormation Git sync to link CloudFormation with the repository. Configure automatic change set creation for pull request updates. Configure CloudFormation to post the change set summary and apply the update when the pull request is approved and merged.

Use CloudFormation drift detection. Create an Amazon EventBridge rule that reacts to pull request updates. Configure the EventBridge rule to produce drift comparison results and send the results as comments before the stack is updated.

Use CloudFormation Lambda Hooks. Configure Lambda Hooks to evaluate the updated template and write the planned changes into the pull request when a stack update is requested.