Free AWS Certified Solutions Architect – Professional Exam SAP-C02 Exam Practice Test

A flood monitoring agency has deployed more than 10.000 water-level monitoring sensors. Sensors send continuous data updates, and each update is less than 1 MB in size. The agency has a fleet of on-premises application servers. These servers receive upda.es 'on the sensors, convert the raw data into a human readable format, and write the results loan on-premises relational database server. Data analysts then use simple SOL queries to monitor the data.The agency wants to increase overall application availability and reduce the effort that is required to perform maintenance tasks These maintenance tasks, which include updates and patches to the application servers, cause downtime. While an application server is down, data is lost from sensors because the remaining servers cannot handle the entire workload.The agency wants a solution that optimizes operational overhead and costs. A solutions architect recommends the use of AWS loT Core to collect the sensor data.What else should the solutions architect recommend to meet these requirements?

• Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to .csv format, and insert it into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
• Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to Apache Parquet format and save it to an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.
• Send the sensor data to an Amazon Managed Service for Apache Flink {previously known as Amazon Kinesis Data Analytics) application to convert the data to .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
• Send the sensor data to an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to convert the data to Apache Parquet format and store it in an Amazon S3 bucket Instruct the data analysis to query the data by using Amazon Athena.

A company is migrating an application to the AWS Cloud. The application runs in an on-premises data center and writes thousands of images into a mounted NFS file system each night. After the company migrates the application, the company will host the application on an Amazon EC2 instance with a mounted AmazonElastic File System (Amazon EFS) file system.The company has established an AWS Direct Connect connection to AWS. Before the migration cutover, a solutions architect must build a process that will replicate the newly created on-premises images to the EFS file system.What is the MOST operationally efficient way to replicate the images?

• Configure a periodic process to run the aws s3 sync command from the on-premises file system to Amazon S3. Configure an AWS Lambda function to process event notifications from Amazon S3 and copy the images from Amazon S3 to the EFS file system.
• Deploy an AWS Storage Gateway file gateway with an NFS mount point. Mount the file gateway file system on the on-premises server. Configure a process to periodically copy the images to the mount point.
• Deploy an AWS DataSync agent to an on-premises server that has access to the NFS file system. Send data over the Direct Connect connection to an S3 bucket by using public VIF. Configure an AWS Lambda function to process event notifications from Amazon S3 and copy the images from Amazon S3 to the EFS file system.
• Deploy an AWS DataSync agent to an on-premises server that has access to the NFS file system. Send data over the Direct Connect connection to an AWS PrivateLink int

A solutions architect needs to improve an application that is hosted in the AWS Cloud. The application uses an Amazon Aurora MySQL DB instance that is experiencing overloaded connections. Most of the application's operations insert records into the database. The application currently stores credentials in a text-based configuration file.The solutions architect needs to implement a solution so that the application can handle the current connection load. The solution must keep the credentials secure and must provide the ability to rotate the credentials automatically on a regular basis.Which solution will meet these requirements?

• Deploy an Amazon RDS Proxy layer in front of the DB instance. Store the connection credentials as a secret in AWS Secrets Manager.
• Deploy an Amazon RDS Proxy layer in front of the DB instance. Store the connection credentials in AWS Systems Manager Parameter Store.
• Create an Aurora Replic a. Store the connection credentials as a secret in AWS Secrets Manager.
• Create an Aurora Replica. Store the connection credentials in AWS Systems Manager Parameter Store.

An online survey company runs its application in the AWS Cloud. The application is distributed and consists of microservices that run in an automatically scaled Amazon Elastic Container Service (Amazon ECS) cluster. The ECS cluster is a target for an Application Load Balancer (ALB). The ALB is a custom origin for an Amazon CloudFront distribution.The company has a survey that contains sensitive data. The sensitive data must be encrypted when it moves through the application. The application's data-handling microservice is the only microservice that should be able to decrypt the data.Which solution will meet these requirements?

• Create a symmetric AWS Key Management Service (AWS KMS) key that is dedicated to the data-handling microservice. Create a field-level encryption profile and a configuration. Associate the KMS key and the configuration with the CloudFront cache behavior.
• Create an RSA key pair that is dedicated to the data-handling microservice. Upload the public key to the CloudFront distribution. Create a field-level encryption profile and a configuration. Add the configuration to the CloudFront cache behavior.
• Create a symmetric AWS Key Management Service (AWS KMS) key that is dedicated to the data-handling microservice. Create a Lambda@Edge function. Program the function to use the KMS key to encrypt the sensitive data.
• Create an RSA key pair that is dedicated to the data-handling microservice. Create a Lambda@Edge function. Program the function to use the private key of the RSA key pair to encrypt the sensitive data.

A company is running a traditional web application on Amazon EC2 instances. The company needs to refactor the application as microservices that run on containers. Separate versions of the application exist in two distinct environments: production and testing. Load for the application is variable, but the minimum load and the maximum load are known. A solutions architect needs to design the updated application with a serverless architecture that minimizes operational complexity.Which solution will meet these requirements MOST cost-effectively?

• Upload the container images to AWS Lambda as functions. Configure a concurrency limit for the associated Lambda functions to handle the expected peak load. Configure two separate Lambda integrations within Amazon API Gateway: one for production and one for testing.
• Upload the container images to Amazon Elastic Container Registry (Amazon ECR). Configure two auto scaled Amazon Elastic Container Service (Amazon ECS) clusters with the Fargate launch type to handle the expected load. Deploy tasks from the ECR images. Configure two separate Application Load Balancers to direct traffic to the ECS clusters.
• Upload the container images to Amazon Elastic Container Registry (Amazon ECR). Configure two auto scaled Amazon Elastic Kubernetes Service (Amazon EKS) clusters with the Fargate launch type to handle the expected load. Deploy tasks from the ECR images. Configure two separate Application Load Balancers to direct traffic to the EKS clusters.
• Upload the container images to AWS Elastic Beanstalk. In Elastic Beanstalk, create separate environments and deployments for production and testing. Configure two separate Application Load Balancers to direct traffic to the Elastic Beanstalk deployments.

A company uses AWS Organizations to manage its AWS accounts. The company needs a list of all its Amazon EC2 instances that have underutilized CPU or memory usage. The company also needs recommendations for how to downsize these underutilized instances.Which solution will meet these requirements with the LEAST effort?

• Install a CPU and memory monitoring tool from AWS Marketplace on all the EC2 Instances. Store the findings in Amazon S3. Implement a Python script to identify underutilized instances. Reference EC2 instance pricing information for recommendations about downsizing options.
• Install the Amazon CloudWatch agent on all the EC2 instances by using AWS Systems Manager. Retrieve the resource op! nization recommendations from AWS Cost Explorer in the organization's management account. Use the recommendations to downsize underutilized instances in all accounts of the organization.
• Install the Amazon CloudWatch agent on all the EC2 instances by using AWS Systems Manager. Retrieve the resource optimization recommendations from AWS Cost Explorer in each account of the organization. Use the recommendations to downsize underutilized instances in all accounts of the organization.
• Install the Amazon CloudWatch agent on all the EC2 instances by using AWS Systems Manager Create an AWS Lambda function to extract CPU and memory usage from all the EC2 instances. Store the findings as files in Amazon S3. Use Amazon Athena to find underutilized instances. Reference EC2 instance pricing information for recommendations about downsizing options.

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 long-term operational overhead.Which solution will meet these requirements?

• 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 API. Host the new application tier on EC2 instances.
• 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.
• Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine learning (Al/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.
• 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.

A solutions architect is designing a solution to process events. The solution must have the ability to scale in and out based on the number of events that the solution receives. If a processing error occurs, the event must move into a separate queue for review.Which solution will meet these requirements?

• Send event details to an Amazon Simple Notification Service (Amazon SNS) topic. Configure an AWS Lambda function as a subscriber to the SNS topic to process the events. Add an on-failure destination to the function. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target.
• Publish events to an Amazon Simple Queue Service (Amazon SQS) queue. Create an Amazon EC2 Auto Scaling group. Configure the Auto Scaling group to scale in and out based on the ApproximateAgeOfOldestMessage metric of the queue. Configure the application to write failed messages to a dead-letter queue.
• Write events to an Amazon DynamoDB table. Configure a DynamoDB stream for the table. Configure the stream to invoke an AWS Lambda function. Configure the Lambda function to process the events.
• Publish events to an Amazon EventBridge event bus. Create and run an application on an Amazon EC2 instance with an Auto Scaling group that is behind an Application Load Balancer (ALB). Set the ALB as the event bus target. Configure the event bus to retry events. Write messages to a dead-letter queue if the application cannot process the messages.

A company is collecting a large amount of data from a fleet of loT devices Data is stored as Optimized Row Columnar (ORC) files in the Hadoop Distributed File System (HDFS) on a persistent Amazon EMR cluster. The company's data analytics team queries the data by using SQL in Apache Presto deployed on the same EMR cluster Queries scan large amounts of data, always run for less than 15 minutes, and run only between 5 PM and 10 PM.The company is concerned about the high cost associated with the current solution A solutions architect must propose the most cost-effective solution that will allow SQL data queriesWhich solution will meet these requirements?

• Store data in Amazon S3 Use Amazon Redshift Spectrum to query data.
• Store data in Amazon S3 Use the AWS Glue Data Catalog and Amazon Athena to query data
• Store data in EMR File System (EMRFS) Use Presto in Amazon EMR to query data
• Store data in Amazon Redshift. Use Amazon Redshift to query data.

An adventure company has launched a new feature on its mobile app. Users can use the feature to upload their hiking and ratting photos and videos anytime. The photos and videos are stored in Amazon S3 Standard storage in an S3 bucket and are served through Amazon CloudFront.The company needs to optimize the cost of the storage. A solutions architect discovers that most of the uploaded photos and videos are accessed infrequently after 30 days. However, some of the uploaded photos and videos are accessed frequently after 30 days. The solutions architect needs to implement a solution that maintains millisecond retrieval availability of the photos and videos at the lowest possible cost.Which solution will meet these requirements?

• Configure S3 Intelligent-Tiering on the S3 bucket.
• Configure an S3 Lifecycle policy to transition image objects and video objects from S3 Standard to S3 Glacier Deep Archive after 30 days.
• Replace Amazon S3 with an Amazon Elastic File System (Amazon EFS) file system that is mounted on Amazon EC2 instances.
• Add a Cache-Control: max-age header to the S3 image objects and S3 video objects. Set the header to 30 days.