FREE PDF QUIZ LATEST SAP-C02 - RELIABLE AWS CERTIFIED SOLUTIONS ARCHITECT - PROFESSIONAL (SAP-C02) TEST VOUCHER

Free PDF Quiz Latest SAP-C02 - Reliable AWS Certified Solutions Architect - Professional (SAP-C02) Test Voucher

Free PDF Quiz Latest SAP-C02 - Reliable AWS Certified Solutions Architect - Professional (SAP-C02) Test Voucher

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Amazon SAP-C02 (AWS Certified Solutions Architect - Professional (SAP-C02)) certification exam is designed for individuals who aspire to demonstrate their advanced skills and expertise in designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Professional (SAP-C02) certification is intended for professionals who have already obtained the AWS Certified Solutions Architect - Associate certification and have significant experience designing and deploying cloud-based solutions on AWS.

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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q362-Q367):

NEW QUESTION # 362
A global media company is planning a multi-Region deployment of an application. Amazon DynamoDB global tables will back the deployment to keep the user experience consistent across the two continents where users are concentrated. Each deployment will have a public Application Load Balancer (ALB). The company manages public DNS internally. The company wants to make the application available through an apex domain.
Which solution will meet these requirements with the LEAST effort?

  • A. Place a Network Load Balancer (NLB) in front of the ALB. Migrate public DNS to Amazon Route 53.
    Create a CNAME record for the apex domain to point to the NLB's static IP address. Use a geolocation routing policy to route traffic based on user location.
  • B. Create an AWS Global Accelerator accelerator with multiple endpoint groups that target endpoints in appropriate AWS Regions. Use the accelerator's static IP address to create a record in public DNS for the apex domain.
  • C. Create an Amazon API Gateway API that is backed by AWS Lambda in one of the AWS Regions.
    Configure a Lambda function to route traffic to application deployments by using the round robin method. Create CNAME records for the apex domain to point to the API's URL.
  • D. Migrate public DNS to Amazon Route 53. Create CNAME records for the apex domain to point to the ALB. Use a geolocation routing policy to route traffic based on user location.

Answer: B

Explanation:
Explanation
AWS Global Accelerator is a service that directs traffic to optimal endpoints (in this case, the Application Load Balancer) based on the health of the endpoints and network routing. It allows you to create an accelerator that directs traffic to multiple endpoint groups, one for each Region where the application is deployed. The accelerator uses the AWS global network to optimize the traffic routing to the healthy endpoint.
By using Global Accelerator, the company can use a single static IP address for the apex domain, and traffic will be directed to the optimal endpoint based on the user's location, without the need for additional load balancers or routing policies.


NEW QUESTION # 363
A company is implementing a serverless architecture by using AWS Lambda functions that need to access a Microsoft SQL Server DB instance on Amazon RDS. The company has separate environments for development and production, including a clone of the database system.
The company's developers are allowed to access the credentials for the development database.
However, the credentials for the production database must be encrypted with a key that only members of the IT security team's IAM user group can access. This key must be rotated on a regular basis.
What should a solutions architect do in the production environment to meet these requirements?

  • A. Store the database credentials in AWS Systems Manager Parameter Store by using a SecureString parameter that is encrypted by an AWS Key Management Service (AWS KMS) customer managed key. Attach a role to each Lambda function to provide access to the SecureString parameter. Restrict access to the SecureString parameter and the customer managed key so that only the IT security team can access the parameter and the key.
  • B. Store the database credentials in the environment variables of each Lambda function. Encrypt the environment variables by using an AWS Key Management Service (AWS KMS) customer managed key. Restrict access to the customer managed key so that only the IT security team can access the key.
  • C. Encrypt the database credentials by using the AWS Key Management Service (AWS KMS) default Lambda key. Store the credentials in the environment variables of each Lambda function.
    Load the credentials from the environment variables in the Lambda code. Restrict access to the KMS key so that only the IT security team can access the key.
  • D. Store the database credentials in AWS Secrets Manager as a secret that is associated with an AWS Key Management Service (AWS KMS) customer managed key. Attach a role to each Lambda function to provide access to the secret. Restrict access to the secret and the customer managed key so that only the IT security team can access the secret and the key.

Answer: D


NEW QUESTION # 364
A company developed a pilot application by using AWS Elastic Beanstalk and Java. To save costs during development, the company's development team deployed the application into a single-instance environment.
Recent tests indicate that the application consumes more CPU than expected. CPU utilization is regularly greater than 85%, which causes some performance bottlenecks.
A solutions architect must mitigate the performance issues before the company launches the application to production.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Modify the existing environment's capacity configuration to use a load-balanced environment type.
    Select all Availability Zones. Add a scale-out rule that will run if the average CPU utilization is over
    85% for 5 minutes.
  • B. Create a new Elastic Beanstalk application. Select a load-balanced environment type. Select all Availability Zones. Add a scale-out rule that will run if the maximum CPU utilization is over 85% for 5 minutes.
  • C. Create a second Elastic Beanstalk environment. Apply the traffic-splitting deployment policy. Specify a percentage of incoming traffic to direct to the new environment in the average CPU utilization is over
    85% for 5 minutes.
  • D. Select the Rebuild environment action with the load balancing option Select an Availability Zones Add a scale-out rule that will run if the sum CPU utilization is over 85% for 5 minutes.

Answer: A

Explanation:
Explanation
This solution will meet the requirements with the least operational overhead because it allows the company to modify the existing environment's capacity configuration, so it becomes a load-balanced environment type. By selecting all availability zones, the company can ensure that the application is running in multiple availability zones, which can help to improve the availability and scalability of the application. The company can also add a scale-out rule that will run if the average CPU utilization is over 85% for 5 minutes, which can help to mitigate the performance issues. This solution does not require creating new Elastic Beanstalk environments or rebuilding the existing one, which reduces the operational overhead.
You can refer to the AWS Elastic Beanstalk documentation for more information on how to use this service:
https://aws.amazon.com/elasticbeanstalk/ You can refer to the AWS documentation for more information on how to use autoscaling: https://aws.amazon.com/autoscaling/


NEW QUESTION # 365
A start up company hosts a fleet of Amazon EC2 instances in private subnets using the latest Amazon Linux 2 AMI. The company's engineers rely heavily on SSH access to the instances for troubleshooting.
The company's existing architecture includes the following:
* A VPC with private and public subnets, and a NAT gateway
* Site-to-Site VPN for connectivity with the on-premises environment
* EC2 security groups with direct SSH access from the on-premises environment The company needs to increase security controls around SSH access and provide auditing of commands executed by the engineers.
Which strategy should a solutions architect use?

  • A. Update the EC2 security groups to only allow inbound TCP on port 22 to the IP addresses of the engineer's devices. Install the Amazon CloudWatch agent on all EC2 instances and send operating system audit logs to CloudWatch Logs.
  • B. Update the EC2 security groups to only allow inbound TCP on port 22 to the IP addresses of the engineer's devices. Enable AWS Config for EC2 security group resource changes. Enable AWS Firewall Manager and apply a security group policy that automatically remediates changes to rules.
  • C. Install and configure EC2 Instance Connect on the fleet of EC2 instances. Remove all security group rules attached to EC2 instances that allow inbound TCP on port 22. Advise the engineers to remotely access the instances by using the EC2 Instance Connect CLI.
  • D. Create an IAM role with the AmazonSSMManagedInstanceCore managed policy attached. Attach the IAM role to all the EC2 instances. Remove all security group rules attached to the EC2 instances that allow inbound TCP on port 22. Have the engineers install the AWS Systems Manager Session Manager plugin for their devices and remotely access the instances by using the start-session API call from Systems Manager.

Answer: D

Explanation:
Explanation
Allows client machines to be able to connect to Session Manager using the AWS CLI instead of going through the AWS EC2 or AWS Server Manager console.
https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.htm
https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.htm


NEW QUESTION # 366
A manufacturing company is building an inspection solution for its factory. The company has IP cameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images.
The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factory's internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers.
How should the company deploy the ML model to meet these requirements?

  • A. Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.
  • B. Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.
  • C. Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.
  • D. Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.

Answer: A

Explanation:
The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports ML inference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.
The other options are not correct because:
Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory's internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.
Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.
Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.
Reference:
https://aws.amazon.com/greengrass/
https://docs.aws.amazon.com/greengrass/v2/developerguide/use-machine-learning-inference.html
https://aws.amazon.com/snowball/
https://aws.amazon.com/monitron/


NEW QUESTION # 367
......

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