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NEW QUESTION 45
A solutions architect has been assigned to migrate a 50 TB Oracle data warehouse that contains sales data from on-premises to Amazon Redshift Major updates to the sales data occur on the final calendar day of the month For the remainder of the month, the data warehouse only receives minor daily updates and is primarily used for reading and reporting Because of this the migration process must start on the first day of the month and must be complete before the next set of updates occur. This provides approximately 30 days to complete the migration and ensure that the minor daily changes have been synchronized with the Amazon Redshift data warehouse Because the migration cannot impact normal business network operations, the bandwidth allocated to the migration for moving data over the internet is 50 Mbps. The company wants to keep data migration costs low
Which steps will allow the solutions architect to perform the migration within the specified timeline?

A. Create an AWS Snowball import job. Configure a server in the company's data center with an extraction agent. Use AWS SCT to manage the extraction agent and convert the Oracle schema to an Amazon Redshift schema. Create a new project in AWS SCT using the registered data extraction agent. Create a local task and an AWS DMS task in AWS SCT with replication of ongoing changes. Copy data to the Snowball device and return the Snowball device to AWS. Allow AWS DMS to copy data from Amazon S3 to Amazon Redshift. Verify that the data migration is complete and perform the cut over to Amazon Redshift.B. Install Oracle database software on an Amazon EC2 instance Configure VPN connectivity between AWS and the company's data center Configure the Oracle database running on Amazon EC2 to join the Oracle Real Application Clusters (RAC) When the Oracle database on Amazon EC2 finishes synchronizing, create an AWS DMS ongoing replication task to migrate the data from the Oracle database on Amazon EC2 to Amazon Redshift Verify the data migration is complete and perform the cut over to Amazon Redshift.C. Install Oracle database software on an Amazon EC2 instance To minimize the migration time configure VPN connectivity between AWS and the company's data center by provisioning a 1 Gbps AWS Direct Connect connection Configure the Oracle database running on Amazon EC2 to be a read replica of the data center Oracle database Start the synchronization process between the company's on-premises data center and the Oracle database on Amazon EC2 When the Oracle database on Amazon EC2 is synchronized with the on-premises database create an AWS DMS ongoing replication task from the Oracle database read replica that is running on Amazon EC2 to Amazon Redshift Verify the data migration is complete and perform the cut over to Amazon Redshift.D. Create an AWS Snowball import job Export a backup of the Oracle data warehouse Copy the exported data to the Snowball device Return the Snowball device to AWS Create an Amazon RDS for Oracle database and restore the backup file to that RDS instance Create an AWS DMS task to migrate the data from the RDS for Oracle database to Amazon Redshift Copy daily incremental backups from Oracle in the data center to the RDS for Oracle database over the internet Verify the data migration is complete and perform the cut over to Amazon Redshift.

Answer: A

 

NEW QUESTION 46
A solutions architect must analyze a company's Amazon EC2 Instances and Amazon Elastic Block Store (Amazon EBS) volumes to determine whether the company is using resources efficiently. The company is running several large, high-memory EC2 instances lo host database dusters that are deployed in active/passive configurations. The utilization of these EC2 instances varies by the applications that use the databases, and the company has not identified a pattern
The solutions architect must analyze the environment and take action based on the findings.
Which solution meets these requirements MOST cost-effectively?

A. Create a dashboard by using AWS Systems Manager OpsConter Configure visualizations tor Amazon CloudWatch metrics that are associated with the EC2 instances and their EBS volumes Review the dashboard periodically and identify usage patterns Rightsize the EC2 instances based on the peaks in the metricsB. Install the Amazon CloudWatch agent on each of the EC2 Instances Turn on AWS Compute Optimizer, and let it run for at least 12 hours Review the recommendations from Compute Optimizer, and rightsize the EC2 instances as directedC. Sign up for the AWS Enterprise Support plan Turn on AWS Trusted Advisor Wait 12 hours Review the recommendations from Trusted Advisor, and rightsize the EC2 instances as directedD. Turn on Amazon CloudWatch detailed monitoring for the EC2 instances and their EBS volumes Create and review a dashboard that is based on the metrics Identify usage patterns Rightsize the FC? instances based on the peaks In the metrics

Answer: B

 

NEW QUESTION 47
A company is migrating applications from on premises to the AWS Cloud. These applications power the company's internal web forms. These web forms collect data for specific events several times each quarter.
The web forms use simple SQL statements to save the data to a local relational database.
Data collection occurs for each event, and the on-premises servers are idle most of the time. The company needs to minimize the amount of idle infrastructure that supports the web forms.
Which solution will meet these requirements?

A. Create Docker images for each server of the legacy web form applications. Create an Amazon Elastic Container Service (Amazon ECS) cluster on AWS Fargate. Place an Application Load Balancer in front of the ECS cluster. Use Fargate task storage to store the web form data.B. Use Amazon EC2 Image Builder to create AMIs for the legacy servers. Use the AMIs to provision EC2 instances to recreate the applications in the AWS.
Cloud. Place an Application Load Balancer (ALB) in front of the EC2 instances. Use Amazon Route 53 to point the DNS names of the web forms to the ALB.C. Provision an Amazon Aurora Serverless cluster. Build multiple schemas for each web form's data storage. Use Amazon API Gateway and an AWS Lambda function to recreate the data input forms. Use Amazon Route 53 to point the DNS names of the web forms to their corresponding API Gateway endpoint.D. Create one Amazon DynamoDB table to store data for all the data input Use the application form name as the table key to distinguish data items. Create an Amazon Kinesis data stream to receive the data input and store the input in DynamoDB. Use Amazon Route 53 to point the DNS names of the web forms to the Kinesis data stream's endpoint.

Answer: C

Explanation:
Explanation
Provision an Amazon Aurora Serverless cluster. Build multiple schemas for each web forms data storage. Use Amazon API Gateway and an AWS Lambda function to recreate the data input forms. Use Amazon Route 53 to point the DNS names of the web forms to their corresponding API Gateway endpoint.

 

NEW QUESTION 48
A company has created an OU in AWS Organizations for each of its engineering teams Each OU owns multiple AWS accounts. The organization has hundreds of AWS accounts A solutions architect must design a solution so that each OU can view a breakdown of usage costs across its AWS accounts.
Which solution meets these requirements?

A. Create an AWS Cost and Usage Report (CUR) from the AWS Organizations management account- Allow each team to visualize the CUR through an Amazon QuickSight dashboardB. Create an AWS Cost and Usage Report (CUR) for each OU by using AWS Resource Access Manager Allow each team to visualize the CUR through an Amazon QuickSight dashboard.C. Create an AWS Cost and Usage Report (CUR) in each AWS Organizations member account Allow each team to visualize the CUR through an Amazon QuickSight dashboard.D. Create an AWS Cost and Usage Report (CUR) by using AWS Systems Manager Allow each team to visualize the CUR through Systems Manager OpsCenter dashboards

Answer: A

 

NEW QUESTION 49
A solutions architect is responsible (or redesigning a legacy Java application to improve its availability, data durability, and scalability. Currently, the application runs on a single high-memory Amazon EC2 instance. It accepts HTTP requests from upstream clients, adds them to an in-memory queue, and responds with a 200 status. A separate application thread reads items from the queue, processes them, and persists the results to an Amazon RDS MySQL instance. The processing time for each item takes 90 seconds on average, most of which is spent waiting on external service calls, but the application is written to process multiple items in parallel.
Traffic to this service is unpredictable. During periods of high load, items may sit in the internal queue for over an hour while the application processes the backlog. In addition, the current system has issues with availability and data loss if the single application node fails.
Clients that access this service cannot be modified. They expect to receive a response to each HTTP request they send within 10 seconds before they will time out and retry the request.
Which approach would improve the availability and durability of (he system while decreasing the processing latency and minimizing costs?

A. Update the application to use a Redis task queue instead of the in-memory queue. 8uild a Docker container image for the application. Create an Amazon ECS task definition that includes the application container and a separate container to host Redis. Deploy the new task definition as an ECS service using AWS Fargate, and enable Auto Scaling.B. Modify the application to use Amazon DynamoDB instead of Amazon RDS. Configure Auto Scaling for the DynamoDB table. Deploy the application within an Auto Scaling group with a scaling policy based on CPU utilization. Back the in-memory queue with a memory-mapped file to an instance store volume and periodically write that file to Amazon S3.C. Create an Amazon API Gateway REST API that uses a service proxy to put items in an Amazon SOS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SOS instead of an in-memory queue. Deploy the new processing application to smaller EC2 instances within an Auto Scaling group that scales dynamically based on the approximate number of messages in the Amazon SOS queue.D. Create an Amazon API Gateway REST API that uses Lambda proxy integration to pass requests to an AWS Lambda function. Migrate the core processing code to a Lambda function and write a wrapper class that provides a handler method that converts the proxy events to the internal application data model and invokes the processing module.

Answer: C

Explanation:
Explanation
The obvious challenges here are long workloads, scalability based on queue load, and reliability. Almost always the defacto answer to queue related workload is SQS. Since the workloads are very long (90 minutes) Lambdas cannot be used (15 mins max timeout). So, autoscaled smaller EC2 nodes that wait on external services to complete the task makes more sense. If the task fails, the message is returned to the queue and retried.

 

NEW QUESTION 50
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