A Solutions Architect is designing a network solution for a company that has applications running in a data center in Northern Virginia. The applications in the company's data center require predictable performance to applications running in a virtual private cloud (VPC) located in us-east-1, and a secondary VPC in uswest-2 within the same account. The company data center is collocated in an AWS Direct Connect facility that serves the us-east-1 region. The company has already ordered an AWS Direct Connect connection and a cross-connect has been established.
Which solution will meet the requirements at the LOWEST cost?
A. Provision a Direct Connect gateway and attach the virtual private gateway (VGW) for the VPC in useast-1 and the VGW for the VPC in us-west-2. Create a private VIF on the Direct Connect connection and associate it to the Direct Connect gateway.
B. Create private VIFs on the Direct Connect connection for each of the company's VPCs in the us-east-1 and us-west-2 regions. Configure the company's data center router to connect directly with the VPCs in those regions via the private VIFs.
C. Deploy a transit VPC solution using Amazon EC2-based router instances in the us-east-1 region. Establish IPsec VPN tunnels between the transit routers and virtual private gateways (VGWs) located in the us-east-1 and us-west-2 regions, which are attached to the company's VPCs in those regions. Create a public VIF on the Direct Connect connection and establish IPsec VPN tunnels over the public VIF between the transit routers and the company's data center router.
D. Order a second Direct Connect connection to a Direct Connect facility with connectivity to the us-west2 region. Work with a partner to establish a network extension link over dark fiber from the Direct Connect facility to the company's data center. Establish private VIFs on the Direct Connect connections for each of the company's VPCs in the respective regions. Configure the company's data center router to connect directly with the VPCs in those regions via the private VIFs.
A company is building an AWS landing zone and has asked a Solutions Architect to design a multi-account access strategy that will allow hundreds of users to use corporate credentials to access the AWS Console. The company is running a Microsoft Active Directory, and users will use an AWS Direct Connect connection to connect to AWS. The company also wants to be able to federate to third-party services and providers, including custom applications.
Which solution meets the requirements by using the LEAST amount of management overhead?
A. Connect the Active Directory to AWS by using single sign-on and an Active Directory Federation Services (AD FS) with SAML 2.0, and then configure the Identity Provider (IdP) system to use form-based authentication. Build the AD FS portal page with corporate branding, and integrate third-party applications that support SAML 2.0 as required.
B. Create a two-way Forest trust relationship between the on-premises Active Directory and the AWS Directory Service. Set up AWS Single Sign-On with AWS Organizations. Use single sign-on integrations for connections with third-party applications.
C. Configure single sign-on by connecting the on-premises Active Directory using the AWS Directory Service AD Connector. Enable federation to the AWS services and accounts by using the IAM applications and services linking function.
Leverage third-party single sign-on as needed.
D. Connect the company's Active Directory to AWS by using AD FS and SAML 2.0. Configure the AD FS claim rule to leverage Regex and a common Active Directory naming convention for the security group to allow federation of all AWS accounts. Leverage third-party single sign-on as needed, and add it to the AD FS server.
A company uses an Amazon EMR cluster to process data once a day. The raw data comes from Amazon S3, and the resulting processed data is also stored in Amazon S3. The processing must complete within 4 hours; currently, it only takes 3 hours. However, the processing time is taking 5 to 10 minutes longer each week due to an increasing volume of raw data.
The team is also concerned about rising costs as the compute capacity increases. The EMR cluster is currently running on three m3.xlarge instances (one master and two core nodes).
Which of the following solutions will reduce costs related to the increasing compute needs?
A. Add additional task nodes, but have the team purchase an all-upfront convertible Reserved Instance for each additional node to offset the costs.
B. Add additional task nodes, but use instance fleets with the master node in on-Demand mode and a mix of On-Demand and Spot Instances for the core and task nodes. Purchase a scheduled Reserved Instance for the master node.
C. Add additional task nodes, but use instance fleets with the master node in Spot mode and a mix of On-Demand and Spot Instances for the core and task nodes. Purchase enough scheduled Reserved Instances to offset the cost of running any On-Demand instances.
D. Add additional task nodes, but use instance fleets with the master node in On-Demand mode and a mix of On-Demand and Spot Instances for the core and task nodes. Purchase a standard all-upfront Reserved Instance for the master node.
A Solutions Architect is designing the storage layer for a data warehousing application. The data files are large, but they have statically placed metadata at the beginning of each file that describes the size and placement of the file's index. The data files are read in by a fleet of Amazon EC2 instances that store the index size, index location, and other category information about the data file in a database. That database is used by Amazon EMR to group files together for deeper analysis.
What would be the MOST cost-effective, high availability storage solution for this workflow?
A. Store the data files in Amazon S3 and use Range GET for each file's metadata, then index the relevant data.
B. Store the data files in Amazon EFS mounted by the EC2 fleet and EMR nodes.
C. Store the data files on Amazon EBS volumes and allow the EC2 fleet and EMR to mount and unmount the volumes where they are needed.
D. Store the content of the data files in Amazon DynamoDB tables with the metadata, index, and data as their own keys.
A finance company is running its business-critical application on current-generation Linux EC2 instances. The application includes a self-managed MySQL database performing heavy I/O operations. The application is working fine to handle a moderate amount of traffic during the month. However, it slows down during the final three days of each month due to month-end reporting, even though the company is using Elastic Load Balancers and Auto Scaling within its infrastructure to meet the increased demand.
Which of the following actions would allow the database to handle the month-end load with the LEAST impact on performance?
A. Pre-warming Elastic Load Balancers, using a bigger instance type, changing all Amazon EBS volumes to GP2 volumes.
B. Performing a one-time migration of the database cluster to Amazon RDS, and creating several additional read replicas to handle the load during end of month.
C. Using Amazon CloudWatch with AWS Lambda to change the type, size, or IOPS of Amazon EBS volumes in the cluster based on a specific CloudWatch metric.
D. Replacing all existing Amazon EBS volumes with new PIOPS volumes that have the maximum available storage size and I/O per second by taking snapshots before the end of the month and reverting back afterwards.
A company is adding a new approved external vendor that only supports IPv6 connectivity. The company's backend systems sit in the private subnet of an Amazon VPC. The company uses a NAT gateway to allow these systems to communicate with external vendors over IPv4. Company policy requires systems that communicate with external vendors to use a security group that limits access to only approved external vendors. The virtual private cloud (VPC) uses the default network ACL.
The Systems Operator successfully assigns IPv6 addresses to each of the backend systems. The Systems Operator also updates the outbound security group to include the IPv6 CIDR of the external vendor (destination). The systems within the VPC are able to ping one another successfully over IPv6. However, these systems are unable to communicate with the external vendor.
What changes are required to enable communication with the external vendor?
A. Create an IPv6 NAT instance. Add a route for destination 0.0.0.0/0 pointing to the NAT instance.
B. Enable IPv6 on the NAT gateway. Add a route for destination ::/0 pointing to the NAT gateway.
C. Enable IPv6 on the internet gateway. Add a route for destination 0.0.0.0/0 pointing to the IGW.
D. Create an egress-only internet gateway. Add a route for destination ::/0 pointing to the gateway.
A company is migrating its marketing website and content management system from an on-premises data center to AWS. The company wants the AWS application to be deployed in a VPC with Amazon EC2 instances used for the web servers and an Amazon RDS instance for the database.
The company has a runbook document that describes the installation process of the on-premises system. The company would like to base the AWS system on the processes referenced in the runbook document. The runbook document describes the installation and configuration of the operating systems, network settings, the website, and content management system software on the servers. After the migration is complete, the company wants to be able to make changes quickly to take advantage of other AWS features.
How can the application and environment be deployed and automated in AWS, while allowing for future changes?
A. Update the runbook to describe how to create the VPC, the EC2 instances, and the RDS instance for the application by using the AWS Console. Make sure that the rest of the steps in the runbook are updated to reflect any changes that may come from the AWS migration.
B. Write a Python script that uses the AWS API to create the VPC, the EC2 instances, and the RDS instance for the application. Write shell scripts that implement the rest of the steps in the runbook. Have the Python script copy and run the shell scripts on the newly created instances to complete the installation.
C. Write an AWS CloudFormation template that creates the VPC, the EC2 instances, and the RDS instance for the application. Ensure that the rest of the steps in the runbook are updated to reflect any changes that may come from the AWS migration.
D. Write an AWS CloudFormation template that creates the VPC, the EC2 instances, and the RDS instance for the application. Include EC2 user data in the AWS CloudFormation template to install and configure the software.
A company wants to migrate its website from an on-premises data center onto AWS. At the same time, it wants to migrate the website to a containerized microservice-based architecture to improve the availability and cost efficiency. The company's security policy states that privileges and network permissions must be configured according to best practice, using least privilege.
A Solutions Architect must create a containerized architecture that meets the security requirements and has deployed the application to an Amazon ECS cluster.
What steps are required after the deployment to meet the requirements? (Choose two.)
A. Create tasks using the bridge network mode.
B. Create tasks using the awsvpc network mode.
C. Apply security groups to Amazon EC2 instances, and use IAM roles for EC2 instances to access other resources.
D. Apply security groups to the tasks, and pass IAM credentials into the container at launch time to access other resources.
E. Apply security groups to the tasks, and use IAM roles for tasks to access other resources.
A large company has many business units. Each business unit has multiple AWS accounts for different purposes. The CIO of the company sees that each business unit has data that would be useful to share with other parts of the company. In total, there are about 10 PB of data that needs to be shared with users in 1,000 AWS accounts. The data is proprietary, so some of it should only be available to users with specific job types. Some of the data is used for throughput of intensive workloads, such as simulations. The number of accounts changes frequently because of new initiatives, acquisitions, and divestitures.
A Solutions Architect has been asked to design a system that will allow for sharing data for use in AWS with all of the employees in the company.
Which approach will allow for secure data sharing in scalable way?
A. Store the data in a single Amazon S3 bucket. Create an IAM role for every combination of job type and business unit that allows for appropriate read/write access based on object prefixes in the S3 bucket. The roles should have trust policies that allow the business unit's AWS accounts to assume their roles. Use IAM in each business unit's AWS account to prevent them from assuming roles for a different job type. Users get credentials to access the data by using AssumeRole from their business unit's AWS account. Users can then use those credentials with an S3 client.
B. Store the data in a single Amazon S3 bucket. Write a bucket policy that uses conditions to grant read and write access where appropriate, based on each user's business unit and job type. Determine the business unit with the AWS account accessing the bucket and the job type with a prefix in the IAM user's name. Users can access data by using IAM credentials from their business unit's AWS account with an S3 client.
C. Store the data in a series of Amazon S3 buckets. Create an application running in Amazon EC2 that is integrated with the company's identity provider (IdP) that authenticates users and allows them to download or upload data through the application. The application uses the business unit and job type information in the IdP to control what users can upload and download through the application. The users can access the data through the application's API.
D. Store the data in a series of Amazon S3 buckets. Create an AWS STS token vending machine that is integrated with the company's identity provider (IdP). When a user logs in, have the token vending machine attach an IAM policy that assumes the role that limits the user's access and/or upload only the data the user is authorized to access. Users can get credentials by authenticating to the token vending machine's website or API and then use those credentials with an S3 client.
A company has a High Performance Computing (HPC) cluster in its on-premises data center, which runs thousands of jobs in parallel for one week every month, processing petabytes of images. The images are stored on a network file server, which is replicated to a disaster recovery site. The on-premises data center has reached capacity and has started to spread the jobs out over the course of the month in order to better utilize the cluster, causing a delay in the job completion.
The company has asked its Solutions Architect to design a cost-effective solution on AWS to scale beyond the current capacity of 5,000 cores and 10 petabytes of data. The solution must require the least amount of management overhead and maintain the current level of durability.
Which solution will meet the company's requirements?
A. Create a container in the Amazon Elastic Container Registry with the executable file for the job. Use Amazon ECS with Spot Fleet in Auto Scaling groups. Store the raw data in Amazon EBS SC1 volumes and write the output to Amazon S3.
B. Create an Amazon EMR cluster with a combination of On Demand and Reserved Instance Task Nodes that will use Spark to pull data from Amazon S3. Use Amazon DynamoDB to maintain a list of jobs that need to be processed by the Amazon EMR cluster.
C. Store the raw data in Amazon S3, and use AWS Batch with Managed Compute Environments to create Spot Fleets. Submit jobs to AWS Batch Job Queues to pull down objects from Amazon S3 onto Amazon EBS volumes for temporary storage to be processed, and then write the results back to Amazon S3.
D. Submit the list of jobs to be processed to an Amazon SQS to queue the jobs that need to be processed. Create a diversified cluster of Amazon EC2 worker instances using Spot Fleet that will automatically scale based on the queue depth. Use Amazon EFS to store all the data sharing it across all instances in the cluster.
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