AWS Certified Solutions Architect - Professional SAP-C02 Practice Test

AWS Certified Solutions Architect - Professional SAP-C02 Practice Test practice questions, page 19 of 21, with answer review and explanations.

Question 91

A company consists or two separate business units. Each business unit has its own AWS account within a single organization in AWS Organizations. The business units regularly share sensitive documents with each other. To facilitate sharing, the company created an Amazon S3 bucket in each account and configured low-way replication between the S3 buckets. The S3 buckets have millions of objects. Recently, a security audit identified that neither S3 bucket has encryption at rest enabled. Company policy requires that all documents must be stored with encryption at rest. The company wants to implement server-side encryption with Amazon S3 managed encryption keys (SSE-S3). What is the MOST operationally efficient solution that meets these requirements?

  1. Turn on SSE-S3 on both S3 buckets. Use S3 Batch Operations to copy and encrypt the objects in the same location.
  2. Create an AWS Key Management Service (AWS KMS) key in each account. Turn on server-side encryption with AWS KMS keys (SSE-KMS) on each S3 bucket by using the corresponding KMS key in that AWS account. Encrypt the existing objects by using an S3 copy command in the AWS CLI.
  3. Turn on SSE-S3 on both S3 buckets. Encrypt the existing objects by using an S3 copy command in the AWS CLI.
  4. Create an AWS Key Management Service, (AWS KMS) key in each account. Turn on server-side encryption with AWS KMS keys (SSE-KMS) on each S3 bucket by using the corresponding KMS key in that AWS account. Use S3 Batch Operations to copy the objects into the same location.

Question 92

A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day. The company needs to query and analyze the data. The company does not access data that is more than 1 year old. However, the company must retain all the data indefinitely for compliance reasons. Which solution will meet these requirements MOST cost-effectively?

  1. Use S3 Select to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
  2. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old 10 S3 Glacier Deep Archive.
  3. Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
  4. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Intelligent-Tiering.

Question 93

A video processing company wants to build a machine learning (ML) model by using 600 TB of compressed data that is stored as thousands of files in the company's on-premises network attached storage system. The company does not have the necessary compute resources on premises for ML experiments and wants to use AWS. The company needs to complete the data transfer to AWS within 3 weeks. The data transfer will be a one-time transfer. The data must be encrypted in transit. The measured upload speed of the company's internet connection is 100 Mbps. and multiple departments share the connection. Which solution will meet these requirements MOST cost-effectively?

  1. Order several AWS Snowball Edge Storage Optimized devices by using the AWS Management Console. Configure the devices with a destination S3 bucket. Copy the data to the devices. Ship the devices back to AWS.
  2. Set up a 10 Gbps AWS Direct Connect connection between the company location and the nearest AWS Region. Transfer the data over a VPN connection into the Region to store the data in Amazon S3.
  3. Create a VPN connection between the on-premises network attached storage and the nearest AWS Region. Transfer the data over the VPN connection.
  4. Deploy an AWS Storage Gateway file gateway on premises. Configure the file gateway with a destination S3 bucket. Copy the data to the file gateway.

Question 94

A company has migrated Its forms-processing application to AWS. When users interact with the application, they upload scanned forms as files through a web application. A database stores user metadata and references to files that are stored in Amazon S3. The web application runs on Amazon EC2 instances and an Amazon RDS for PostgreSQL database. When forms are uploaded, the application sends notifications to a team through Amazon Simple Notification Service (Amazon SNS). A team member then logs in and processes each form. The team member performs data validation on the form and extracts relevant data before entering the information into another system that uses an API. A solutions architect needs to automate the manual processing of the forms. The solution must provide accurate form extraction. minimize time to market, and minimize tong-term operational overhead. Which solution will meet these requirements?

  1. Develop custom libraries to perform optical character recognition (OCR) on the forms. Deploy the libraries to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster as an application tier. Use this tier to process the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data into an Amazon DynamoDB table. Submit the data to the target system's APL. Host the new application tier on EC2 instances.
  2. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use artificial intelligence and machine learning (AI/ML) models that are trained and hosted on an EC2 instance to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
  3. Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine teaming (AI/ML) models that are trained and hosted in Amazon SageMaker to perform optical character recognition (OCR) on the forms. Store the output in Amazon ElastiCache. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
  4. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

Question 95

A company is refactoring its on-premises order-processing platform in the AWS Cloud. The platform includes a web front end that is hosted on a fleet of VMs, RabbitMQ to connect the front end to the backend, and a Kubernetes cluster to run a containerized backend system to process the orders. The company does not want to make any major changes to the application. Which solution will meet these requirements with the LEAST operational overhead?

  1. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
  2. Create a custom AWS Lambda runtime to mimic the web server environment. Create an Amazon API Gateway API to replace the front-end web servers. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
  3. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Install Kubernetes on a fleet of different EC2 instances to host the order-processing backend.
  4. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up an Amazon Simple Queue Service (Amazon SQS) queue to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.