SAP-C02 Exam Dumps - AWS Certified Solutions Architect - Professional

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Question # 121

A company runs an loT platform on AWS loT sensors in various locations send data to the company ' s Node js API servers on Amazon EC2 instances running behind an Application Load Balancer The data is stored in an Amazon RDS MySQL DB instance that uses a 4 TB General Purpose SSD volume

The number of sensors the company has deployed in the field has increased over time and is expected to grow significantly The API servers are consistently overloaded and RDS metrics show high write latency

Which of the following steps together will resolve the issues permanently and enable growth as new sensors are provisioned, while keeping this platform cost-efficient? {Select TWO.)

Resize the MySQL General Purpose SSD storage to 6 TB to improve the volume ' s IOPS

Re-architect the database tier to use Amazon Aurora instead of an RDS MySQL DB instance andadd read replicas

Leverage Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data

Use AWS X-Ray to analyze and debug application issues and add more API servers to match the load

Re-architect the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance

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Answer:

Explanation:

Option C is correct because leveraging Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data resolves the issues permanently and enable growth as new sensors are provisioned. Amazon Kinesis Data Streams is a serverless streaming data service that simplifies the capture, processing, and storage of data streams at any scale. Kinesis Data Streams can handle any amount of streaming data and process data from hundreds of thousands of sources with very low latency. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda can be triggered by Kinesis Data Streams events and process the data records in real time. Lambda can also scale automatically based on the incoming data volume.Â By using Kinesis Data Streams and Lambda, the company can reduce the load on the API servers and improve the performance and scalability of the data ingestion and processing layer3

Option E is correct because re-architecting the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance resolves the issues permanently and enable growth as new sensors are provisioned. Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB supports auto scaling, which automatically adjusts read and write capacity based on actual traffic patterns. DynamoDB also supports on-demand capacity mode, which instantly accommodates up to double the previous peak traffic on a table. By using DynamoDB instead of RDS MySQL DB instance, the company can eliminate high write latency and improve scalability and performance of the database tier.

[References:Â 1: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.htmlÂ 2: https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_AuroraOverview.htmlÂ 3: https://docs.aws.amazon.com/streams/latest/dev/introduction.html : https://docs.aws.amazon.com/lambda/latest/dg/welcome.html : https://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html : https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html :, , , , , , , ]

Question # 122

A company is planning to migrate 1,000 on-premises servers to AWS. The servers run on several VMware clusters in the companyâ€™s data center. As part of the migration plan, the company wants to gather server metrics such as CPU details, RAM usage, operating system information, and running processes. The company then wants to query and analyze the data.

Which solution will meet these requirements?

Deploy and configure the AWS Agentless Discovery Connector virtual appliance on the on-premises hosts. Configure Data Exploration in AWS Migration Hub. Use AWS Glue to perform an ETL job against the data. Query the data by using Amazon S3 Select.

Export only the VM performance information from the on-premises hosts. Directly import the required data into AWS Migration Hub. Update any missing information in Migration Hub. Query the data by using Amazon QuickSight.

Create a script to automatically gather the server information from the on-premises hosts. Use the AWS CLI to run the put-resource-attributes command to store the detailed server data in AWS Migration Hub. Query the data directly in the Migration Hub console.

Deploy the AWS Application Discovery Agent to each on-premises server. Configure Data Exploration in AWS Migration Hub. Use Amazon Athena to run predefined queries against the data in Amazon S3.

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Question # 123

A VPC spans three Availability Zones, each with public and private subnets. One NAT gateway and one internet gateway exist. Private EC2 instances must connect to the internet.

Add two more NAT gateways (one per AZ). Configure each private subnet to use its AZâ€™s NAT gateway.

Add two more NAT gateways and configure public subnets.

Add internet gateways per AZ and route private subnets.

Add internet gateways per AZ and configure public subnets.

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Question # 124

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?

Pre-warming Elastic Load Balancers, using a bigger instance type, changing all Amazon EBS volumes to GP2 volumes.

Performing a one-time migration of the database cluster to Amazon RDS. and creatingseveral additional read replicas to handle the load during end of month

Using Amazon CioudWatch with AWS Lambda to change the type. size, or IOPS of Amazon EBS volumes in the cluster based on a specific CloudWatch metric

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.

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Question # 125

A company that develops consumer electronics with offices in Europe and Asia has 60 TB of software images stored on premises in Europe The company wants to transfer the images to an Amazon S3 bucket in the ap-northeast-1 Region New software images are created daily and must be encrypted in transit The company needs a solution that does not require custom development toautomatically transfer all existing and new software images to Amazon S3

What is the next step in the transfer process?

Deploy an AWS DataSync agent and configure a task to transfer the images to the S3 bucket

Configure Amazon Kinesis Data Firehose to transfer the images using S3 Transfer Acceleration

Use an AWS Snowball device to transfer the images with the S3 bucket as the target

Transfer the images over a Site-to-Site VPN connection using the S3 API with multipart upload

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Question # 126

A company has a sales system that stores transactions as .csv files in an Amazon S3 bucket. The S3 bucket is configured to use S3 Intelligent-Tiering. Most of the .csv files are between 64 KB and 100 KB in size. All rows and columns of the .csv files must be read when the data is processed. The company must keep the data for 5 years.

The company stores several million xsv files every day. The company must minimize the cost of storing and querying the xsv files.

Which solution will meet these requirements?

Create an AWS Glue job to convert the .csv files into Apache Parquet format. Use Amazon S3 to invoke the AWS Glue job every time a .csv file arrives.

Create an AWS Glue job to compress the .csv files. Schedule the AWS Glue job every hour to compress the files for the previous hour into one .csv file.

Create an AWS Lambda function to convert the .csv files into Apache Parquet format. Use Amazon S3 to invoke the Lambda function every time a .csv file arrives.

Create an AWS Lambda function to compress the .csv files. Use Amazon S3 to invoke the Lambda function every time a .csv file arrives.

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Question # 127

A company has an on-premises website application that provides real estate information for potential renters and buyers. The website uses a Java backend and a NOSQL MongoDB database to store subscriber data.

The company needs to migrate the entire application to AWS with a similar structure. The application must be deployed for high availability, and the company cannot make changes to the application

Which solution will meet these requirements?

use an Amazon Aurora DB cluster as the database for the subscriber data. Deploy Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones for the Java backend application.

Use MongoDB on Amazon EC2 instances as the database for the subscriber data. Deploy EC2 instances in an Auto Scaling group in a single Availability Zone for the Java backend application.

Configure Amazon DocumentD3 (with MongoDB compatibility) with appropriately sized instances in multiple Availability Zones as the database for the subscriber data. Deploy Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones for the Java backend application.

Configure Amazon DocumentDB (with MongoDB compatibility) in on-demand capacity mode in multiple Availability Zones as the database for the subscriber data. Deploy Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones for the Java backend application.

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Question # 128

A company has an application that runs on Amazon EC2 instances in an Amazon EC2 Auto Scaling group. The company uses AWS CodePipeline to deploy the application. The instances that run in the Auto Scaling group are constantly changing because of scaling events.

When the company deploys new application code versions, the company installs the AWS CodeDeploy agent on any new target EC2 instances and associates the instances with the CodeDeploy deployment group. The application is set to go live within the next 24 hours.

What should a solutions architect recommend to automate the application deployment process with the LEAST amount of operational overhead?

Configure Amazon EventBridge to invoke an AWS Lambda function when a new EC2 instance is launched into the Auto Scaling group. Code the Lambda function to associate the EC2 instances with the CodeDeploy deployment group.

Write a script to suspend Amazon EC2 Auto Scaling operations before the deployment of new code When the deployment is complete, create a new AMI and configure the Auto Scaling group ' s launch template to use the new AMI for new launches. Resume Amazon EC2 Auto Scaling operations.

Create a new AWS CodeBuild project that creates a new AMI that contains the new code Configure CodeBuild to update the Auto Scaling group ' s launch template to the new AMI. Run an Amazon EC2 Auto Scaling instance refresh operation.

Create a new AMI that has the CodeDeploy agent installed. Configure the Auto Scaling group ' s launch template to use the new AMI. Associate the CodeDeploy deployment group with the Auto Scaling group instead of the EC2 instances.

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