Topic 1: Exam Pool A
A company's HTTP application is behind a Network Load Balancer (NLB). The NLB's target group is configured to use an Amazon EC2 Auto Scaling group with multiple EC2 instances that run the web service.
The company notices that the NLB is not detecting HTTP errors for the application. These errors require a manual restart of the EC2 instances that run the web service. The company needs to improve the application's availability without writing custom scripts or code.
What should a solutions architect do to meet these requirements?
A. Enable HTTP health checks on the NLB. supplying the URL of the company's application.
B. Add a cron job to the EC2 instances to check the local application's logs once each minute. If HTTP errors are detected, the application will restart.
C. Replace the NLB with an Application Load Balancer. Enable HTTP health checks by supplying the URL of the company's application. Configure an Auto Scaling action to replace unhealthy instances.
D. Create an Amazon Cloud Watch alarm that monitors the UnhealthyHostCount metric for the NLB. Configure an Auto Scaling action to replace unhealthy instances when the alarm is in the ALARM state.
Explanation: Application availability: NLB cannot assure the availability of the application. This is because it bases its decisions solely on network and TCP-layer variables and has no awareness of the application at all. Generally, NLB determines availability based on the ability of a server to respond to ICMP ping or to correctly complete the three-way TCP handshake. ALB goes much deeper and is capable of determining availability based on not only a successful HTTP GET of a particular page but also the verification that the content is as was expected based on the input parameters.
An ecommerce company is running a seasonal online sale. The company hosts its website on Amazon EC2 instances spanning multiple Availability Zones. The company wants its website to manage sudden traffic increases during the sale. Which solution will meet these requirements MOST cost-effectively?
A. Create an Auto Scaling group that is large enough to handle peak traffic load. Stop half of the Amazon EC2 instances. Configure the Auto Scaling group to use the stopped instances to scale out when traffic increases.
B. Create an Auto Scaling group for the website. Set the minimum size of the Auto Scaling group so that it can handle high traffic volumes without the need to scale out.
C. Use Amazon CIoudFront and Amazon ElastiCache to cache dynamic content with an Auto Scaling group set as the origin. Configure the Auto Scaling group with the instances necessary to populate CIoudFront and ElastiCache. Scale in after the cache is fully populated.
D. Configure an Auto Scaling group to scale out as traffic increases. Create a launch template to start new instances from a preconfigured Amazon Machine Image (AMI).
Explanation:
The solution that meets the requirements of high availability, resiliency, and minimal operational effort is to use AWS Transfer for SFTP and an Amazon S3 bucket for storage. This solution allows the company to securely transfer files over SFTP to Amazon S3, which is a durable and scalable object storage service. The company can then modify the application to pull the batch files from Amazon S3 to an Amazon EC2 instance for processing. The EC2 instance can be part of an Auto Scaling group with a scheduled scaling policy to run the batch operation only at night. This way, the company can save costs by scaling down the EC2 instances when they are not needed. The other solutions do not meet all the requirements because they either use Amazon EFS or Amazon EBS for storage, which are more expensive and less scalable than Amazon S3, or they do not use a scheduled scaling policy to optimize the EC2 instances usage.
References:
AWS Transfer for SFTP
Amazon S3
Amazon EC2 Auto Scaling
A retail company uses a regional Amazon API Gateway API for its public REST APIs. The API Gateway endpoint is a custom domain name that points to an Amazon Route 53 alias record. A solutions architect needs to create a solution that has minimal effects on customers and minimal data loss to release the new version of APIs. Which solution will meet these requirements?
A. Create a canary release deployment stage for API Gateway. Deploy the latest API version. Point an appropriate percentage of traffic to the canary stage. After API verification, promote the canary stage to the production stage.
B. Create a new API Gateway endpoint with a new version of the API in OpenAPI YAML file format. Use the import-to-update operation in merge mode into the API in API Gateway. Deploy the new version of the API to the production stage.
C. Create a new API Gateway endpoint with a new version of the API in OpenAPI JSON file format. Use the import-to-update operation in overwrite mode into the API in API Gateway. Deploy the new version of the API to the production stage.
D. Create a new API Gateway endpoint with new versions of the API definitions. Create a custom domain name for the new API Gateway API. Point the Route 53 alias record to the new API Gateway API custom domain name.
Explanation: This answer is correct because it meets the requirements of releasing the new version of APIs with minimal effects on customers and minimal data loss. A canary release deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage. In a canary release deployment, total API traffic is separated at random into a production release and a canary release with a pre- configured ratio. Typically, the canary release receives a small percentage of API traffic and the production release takes up the rest. The updated API features are only visible to API traffic through the canary. You can adjust the canary traffic percentage to optimize test coverage or performance. By keeping canary traffic small and the selection random, most users are not adversely affected at any time by potential bugs in the new version, and no single user is adversely affected all the time. After the test metrics pass your requirements, you can promote the canary release to the production release and disable the canary from the deployment. This makes the new features available in the production stage.
An ecommerce company wants to launch a one-deal-a-day website on AWS. Each day will feature exactly one product on sale for a period of 24 hours. The company wants to be able to handle millions of requests each hour with millisecond latency during peak hours.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon S3 to host the full website in different S3 buckets Add Amazon CloudFront distributions Set the S3 buckets as origins for the distributions Store the order data in Amazon S3
B. Deploy the full website on Amazon EC2 instances that run in Auto Scaling groups across multiple Availability Zones Add an Application Load Balancer (ALB) to distribute the website traffic Add another ALB for the backend APIs Store the data in Amazon RDS for MySQL
C. Migrate the full application to run in containers Host the containers on Amazon Elastic Kubernetes Service (Amazon EKS) Use the Kubernetes Cluster Autoscaler to increase and decrease the number of pods to process bursts in traffic Store the data in Amazon RDS for MySQL
D. Use an Amazon S3 bucket to host the website's static content Deploy an Amazon CloudFront distribution. Set the S3 bucket as the origin Use Amazon API Gateway and AWS Lambda functions for the backend APIs Store the data in Amazon DynamoDB
Explanation: To launch a one-deal-a-day website on AWS with millisecond latency during peak hours and with the least operational overhead, the best option is to use an Amazon S3 bucket to host the website's static content, deploy an Amazon CloudFront distribution, set the S3 bucket as the origin, use Amazon API Gateway and AWS Lambda functions for the backend APIs, and store the data in Amazon DynamoDB. This option requires minimal operational overhead and can handle millions of requests each hour with millisecond latency during peak hours. Therefore, option D is the correct answer.
A company hosts its application in the AWS Cloud. The application runs on Amazon EC2 instances behind an Elastic Load Balancer in an Auto Scaling group and with an Amazon DynamoDB table. The ‘company wants to ensure the application can be made available in another AWS Region with minimal downtime. What should a solutions architect do to meet these requirements with the LEAST amount of downtime?
A. Create an Auto Scaling group and a load balancer in the disaster recovery Region. Configure the DynamoDB table as a global table. Configure DNS failover to point to the new disaster recovery Region's load balancer.
B. Create an AWS CloudFormation template to create EC2 instances, load balancers, and DynamoDB tables to be launched when needed. Configure DNS failover to point to the new disaster recovery Region's load balancer.
C. Create an AWS CloudFormation template to create EC2 instances and a load balancer to be launched when needed. Configure the DynamoDB table as a global table. Configure DNS failover to point to the new disaster recovery Region's load balancer.
D. Create an Auto Scaling group and load balancer in the disaster recovery Region. Configure the DynamoDB table as a global table. Create an Amazon CloudWatch alarm to trigger an AWS Lambda function that updates Amazon Route 53 pointing to the disaster recovery load balancer.
Explanation: This answer is correct because it meets the requirements of securely migrating the existing data to AWS and satisfying the new regulation. AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and Amazon S3. DataSync automatically encrypts data in transit and verifies data integrity during transfer. AWS CloudTrail is a service that records AWS API calls for your account and delivers log files to Amazon S3. CloudTrail can log data events, which show the resource operations performed on or within a resource in your AWS account, such as S3 object-level API activity. By using CloudTrail to log data events, you can audit access at all levels of the stored data.
A company is migrating its multi-tier on-premises application to AWS. The application consists of a single-node MySQL database and a multi-node web tier. The company must minimize changes to the application during the migration. The company wants to improve application resiliency after the migration. Which combination of steps will meet these requirements? (Select TWO.)
A. Migrate the web tier to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
B. Migrate the database to Amazon EC2 instances in an Auto Scaling group behind a Network Load Balancer.
C. Migrate the database to an Amazon RDS Multi-AZ deployment.
D. Migrate the web tier to an AWS Lambda function.
E. Migrate the database to an Amazon DynamoDB table.
Explanation: An Auto Scaling group is a collection of EC2 instances that share similar characteristics and can be scaled in or out automatically based on demand. An Auto Scaling group can be placed behind an Application Load Balancer, which is a type of Elastic Load Balancing load balancer that distributes incoming traffic across multiple targets in multiple Availability Zones. This solution will improve the resiliency of the web tier by providing high availability, scalability, and fault tolerance. An Amazon RDS Multi-AZ deployment is a configuration that automatically creates a primary database instance and synchronously replicates the data to a standby instance in a different Availability Zone. When a failure occurs, Amazon RDS automatically fails over to the standby instance without manual intervention. This solution will improve the resiliency of the database tier by providing data redundancy, backup support, and availability. This combination of steps will meet the requirements with minimal changes to the application during the migration.
A development team needs to host a website that will be accessed by other teams. The website contents consist of HTML, CSS, client-side JavaScript, and images Which method is the MOST cost-effective for hosting the website?
A. Containerize the website and host it in AWS Fargate.
B. Create an Amazon S3 bucket and host the website there
C. Deploy a web server on an Amazon EC2 instance to host the website.
D. Configure an Application Loa d Balancer with an AWS Lambda target that uses the Express js framework.
Explanation:In Static Websites, Web pages are returned by the server which are prebuilt. They use simple languages such as HTML, CSS, or JavaScript.
There is no processing of content on the server (according to the user) in Static Websites. Web pages are returned by the server with no change therefore, static Websites are fast.
There is no interaction with databases.
Also, they are less costly as the host does not need to support server-side processing with different languages.
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In Dynamic Websites, Web pages are returned by the server which are processed during runtime means they are not prebuilt web pages but they are built during runtime according to the user’s demand.
These use server-side scripting languages such as PHP, Node.js, ASP.NET and many more supported by the server.
So, they are slower than static websites but updates and interaction with databases are possible.
A company is preparing a new data platform that will ingest real-time streaming data from multiple sources. The company needs to transform the data before writing the data to Amazon S3. The company needs the ability to use SQL to query the transformed data. Which solutions will meet these requirements? (Choose two.)
A. Use Amazon Kinesis Data Streams to stream the data. Use Amazon Kinesis Data Analytics to transform the data. Use Amazon Kinesis Data Firehose to write the data to Amazon S3. Use Amazon Athena to query the transformed data from Amazon S3.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to stream the data. Use AWS Glue to transform the data and to write the data to Amazon S3. Use Amazon Athena to query the transformed data from Amazon S3.
C. Use AWS Database Migration Service (AWS DMS) to ingest the data. Use Amazon EMR to transform the data and to write the data to Amazon S3. Use Amazon Athena to query the transformed data from Amazon S3.
D. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to stream the data. Use Amazon Kinesis Data Analytics to transform the data and to write the data to Amazon S3. Use the Amazon RDS query editor to query the transformed data from Amazon S3.
E. Use Amazon Kinesis Data Streams to stream the data. Use AWS Glue to transform the data. Use Amazon Kinesis Data Firehose to write the data to Amazon S3. Use the Amazon RDS query editor to query the transformed data from Amazon S3.
Explanation: To ingest, transform, and query real-time streaming data from multiple sources, Amazon Kinesis and Amazon MSK are suitable solutions. Amazon Kinesis Data Streams can stream the data from various sources and integrate with other AWS services. Amazon Kinesis Data Analytics can transform the data using SQL or Apache Flink. Amazon Kinesis Data Firehose can write the data to Amazon S3 or other destinations. Amazon Athena can query the transformed data from Amazon S3 using standard SQL. Amazon MSK can stream the data using Apache Kafka, which is a popular open-source platform for streaming data. AWS Glue can transform the data using Apache Spark or Python scripts and write the data to Amazon S3 or other destinations. Amazon Athena can also query the transformed data from Amazon S3 using standard SQL.
A company stores call transcript files on a monthly basis. Users access the files randomly within 1 year of the call, but users access the files infrequently after 1 year. The company wants to optimize its solution by giving users the ability to query and retrieve files that are less than 1-year-old as quickly as possible. A delay in retrieving older files is acceptable. Which solution will meet these requirements MOST cost-effectively?
A. Store individual files with tags in Amazon S3 Glacier Instant Retrieval. Query the tags to retrieve the files from S3 Glacier Instant Retrieval.
B. Store individual files in Amazon S3 Intelligent-Tiering. Use S3 Lifecycle policies to move the files to S3 Glacier Flexible Retrieval after 1 year. Query and retrieve the files that are in Amazon S3 by using Amazon Athena. Query and retrieve the files that are in S3 Glacier by using S3 Glacier Select.
C. Store individual files with tags in Amazon S3 Standard storage. Store search metadata for each archive in Amazon S3 Standard storage. Use S3 Lifecycle policies to move the files to S3 Glacier Instant Retrieval after 1 year. Query and retrieve the files by searching for metadata from Amazon S3.
D. Store individual files in Amazon S3 Standard storage. Use S3 Lifecycle policies to move the files to S3 Glacier Deep Archive after 1 year. Store search metadata in Amazon RDS. Query the files from Amazon RDS. Retrieve the files from S3 Glacier Deep Archive.
Explanation: "For archive data that needs immediate access, such as medical images, news media assets, or genomics data, choose the S3 Glacier Instant Retrieval storage class, an archive storage class that delivers the lowest cost storage with milliseconds retrieval. For archive data that does not require immediate access but needs the flexibility to retrieve large sets of data at no cost, such as backup or disaster recovery use cases, choose S3 Glacier Flexible Retrieval (formerly S3 Glacier), with retrieval in minutes or free bulk retrievals in 5- 12 hours.
A company is moving its on-premises Oracle database to Amazon Aurora PostgreSQL. The database has several applications that write to the same tables. The applications need to be migrated one by one with a month in between each migration. Management has expressed concerns that the database has a high number of reads and writes. The data must be kept in sync across both databases throughout the migration. What should a solutions architect recommend?
A. Use AWS DataSync for the initial migration. Use AWS Database Migration Service (AWS DMS) to create a change data capture (CDC) replication task and a table mapping to select all tables.
B. Use AWS DataSync for the initial migration. Use AWS Database Migration Service (AWS DMS) to create a full load plus change data capture (CDC) replication task and a table mapping to select all tables.
C. Use the AWS Schema Conversion Tool with AWS Database Migration Service (AWS DMS) using a memory optimized replication instance. Create a full load plus change data capture (CDC) replication task and a table mapping to select all tables.
D. Use the AWS Schema Conversion Tool with AWS Database Migration Service (AWS DMS) using a compute optimized replication instance. Create a full load plus change data capture (CDC) replication task and a table mapping to select the largest tables.
A company is launching a new application and will display application metrics on an Amazon CloudWatch dashboard. The company’s product manager needs to access this dashboard periodically. The product manager does not have an AWS account. A solution architect must provide access to the product manager by following the principle of least privilege.
Which solution will meet these requirements?
A. Share the dashboard from the CloudWatch console. Enter the product manager’s email address, and complete the sharing steps. Provide a shareable link for the dashboard to the product manager.
B. Create an IAM user specifically for the product manager. Attach the CloudWatch Read Only Access managed policy to the user. Share the new login credential with the product manager. Share the browser URL of the correct dashboard with the product manager.
C. Create an IAM user for the company’s employees, Attach the View Only Access AWS managed policy to the IAM user. Share the new login credentials with the product manager. Ask the product manager to navigate to the CloudWatch console and locate the dashboard by name in the Dashboards section.
D. Deploy a bastion server in a public subnet. When the product manager requires access to the dashboard, start the server and share the RDP credentials. On the bastion server, ensure that the browser is configured to open the dashboard URL with cached AWS credentials that have appropriate permissions to view the dashboard.
Explanation: To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able to make any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.
A company has a production workload that runs on 1,000 Amazon EC2 Linux instances. The workload is powered by third-party software. The company needs to patch the third- party software on all EC2 instances as quickly as possible to remediate a critical security vulnerability.
What should a solutions architect do to meet these requirements?
A. Create an AWS Lambda function to apply the patch to all EC2 instances.
B. Configure AWS Systems Manager Patch Manager to apply the patch to all EC2 instances.
C. Schedule an AWS Systems Manager maintenance window to apply the patch to all EC2 instances.
D. Use AWS Systems Manager Run Command to run a custom command that applies the patch to all EC2 instances.
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