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The SAP-C02 certification exam is an important milestone for IT professionals who want to advance their careers in cloud computing. It demonstrates their expertise in designing and deploying complex applications on the AWS platform and validates their skills and knowledge to potential employers. By passing the SAP-C02 Exam, candidates can prove their proficiency in managing and deploying cloud-based solutions at an enterprise level.

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The AWS Certified Solutions Architect - Professional (SAP-C02) exam is a three-hour exam consisting of 75 multiple-choice and multiple-response questions. SAP-C02 exam is available in multiple languages and can be taken at any of the AWS testing centers or online proctored exam. SAP-C02 exam fee is $300 USD.

The SAP-C02 exam covers a wide range of topics, including designing and deploying AWS systems, designing and deploying applications, managing data security, managing infrastructure security, managing compliance, and managing cost optimization. SAP-C02 Exam consists of 75 multiple-choice and multiple-response questions that must be completed within 180 minutes. SAP-C02 exam is available in English, Japanese, Korean, and Simplified Chinese.

Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q60-Q65):

NEW QUESTION # 60
A company is hosting a monolithic REST-based API for a mobile app on five Amazon EC2 instances in public subnets of a VPC. Mobile clients connect to the API by using a domain name that is hosted on Amazon Route 53. The company has created a Route 53 multivalue answer routing policy with the IP addresses of all the EC2 instances. Recently, the app has been overwhelmed by large and sudden increases to traffic. The app has not been able to keep up with the traffic. A solutions architect needs to implement a solution so that the app can handle the new and varying load.
Which solution will meet these requirements with the LEAST operational overhead?

A. Separate the API into individual AWS Lambda functions. Configure an Amazon API Gateway REST API with Lambda integration for the backend. Update the Route 53 record to point to the API Gateway API.
B. Containerize the API logic. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
Run the containers in the cluster by using Amazon EC2. Create a Kubernetes ingress. Update the Route 53 record to point to the Kubernetes ingress.
C. Create an Application Load Balancer (ALB) in front of the API. Move the EC2 instances to private subnets in the VPC. Add the EC2 instances as targets for the ALB. Update the Route 53 record to point to the ALB.
D. Create an Auto Scaling group. Place all the EC2 instances in the Auto Scaling group. Configure the Auto Scaling group to perform scaling actions that are based on CPU utilization. Create an AWS Lambda function that reacts to Auto Scaling group changes and updates the Route 53 record.

Answer: A

Explanation:
By breaking down the monolithic API into individual Lambda functions and using API Gateway to handle the incoming requests, the solution can automatically scale to handle the new and varying load without the need for manual scaling actions. Additionally, this option will automatically handle the traffic without the need of having EC2 instances running all the time and only pay for the number of requests and the duration of the execution of the Lambda function. By updating the Route 53 record to point to the API Gateway, the solution can handle the traffic and also it will direct the traffic to the correct endpoint.

NEW QUESTION # 61
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. 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.
B. 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.
C. 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.
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: C

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.
References:
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 # 62
A company uses Amazon S3 to store files and images in a variety of storage classes. The company's S3 costs have increased substantially during the past year.
A solutions architect needs to review data trends for the past 12 months and identity the appropriate storage class for the objects.
Which solution will meet these requirements?

A. Use Amazon S3 Storage Lens. Upgrade the default dashboard to include advanced metrics for storage trends.
B. Use Access Analyzer for S3. Download the Access Analyzer for S3 report for the last 12 months. Import the csvfile to an Amazon QuickSight dashboard.
C. Use S3 storage class analysis. Import data trends into an Amazon QuickSight dashboard to analyze storage trends.
D. Download AWS Cost and Usage Reports for the last 12 months of S3 usage. Review AWS Trusted Advisor recommendations for cost savings.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage_lens.html

NEW QUESTION # 63
A software as a service (SaaS) based company provides a case management solution to customers A3 part of the solution. The company uses a standalone Simple Mail Transfer Protocol (SMTP) server to send email messages from an application. The application also stores an email template for acknowledgement email messages that populate customer data before the application sends the email message to the customer.
The company plans to migrate this messaging functionality to the AWS Cloud and needs to minimize operational overhead.
Which solution will meet these requirements MOST cost-effectively?

A. Set up Amazon Simple Email Service (Amazon SES) to send email messages. Store the email template in an Amazon S3 bucket. Create an AWS Lambda function to retrieve the template from the S3 bucket and to merge the customer data from the application with the template. Use an SDK in the Lambda function to send the email message.
B. Set up an SMTP server on Amazon EC2 instances by using an AMI from the AWS Marketplace. Store the email template in Amazon Simple Email Service (Amazon SES) with parameters for the customer data. Create an AWS Lambda function to call the SES template and to pass customer data to replace the parameters. Use the AWS Marketplace SMTP server to send the email message.
C. Set up Amazon Simple Email Service (Amazon SES) to send email messages. Store the email template on Amazon SES with parameters for the customer data. Create an AWS Lambda function to call the SendTemplatedEmail API operation and to pass customer data to replace the parameters and the email destination.
D. Set up an SMTP server on Amazon EC2 instances by using an AMI from the AWS Marketplace. Store the email template in an Amazon S3 bucket. Create an AWS Lambda function to retrieve the template from the S3 bucket and to merge the customer data from the application with the template. Use an SDK in the Lambda function to send the email message.

Answer: C

Explanation:
In this solution, the company can use Amazon SES to send email messages, which will minimize operational overhead as SES is a fully managed service that handles sending and receiving email messages. The company can store the email template on Amazon SES with parameters for the customer data and use an AWS Lambda function to call the SendTemplatedEmail API operation, passing in the customer data to replace the parameters and the email destination. This solution eliminates the need to set up and manage an SMTP server on EC2 instances, which can be costly and time-consuming.

NEW QUESTION # 64
A retail company has an on-premises data center in Europe. The company also has a multi-Region AWS presence that includes the eu-west-1 and us-east-1 Regions. The company wants to be able to route network traffic from its on-premises infrastructure into VPCs in either of those Regions. The company also needs to support traffic that is routed directly between VPCs in those Regions. No single points of failure can exist on the network.
The company already has created two 1 Gbps AWS Direct Connect connections from its on-premises data center. Each connection goes into a separate Direct Connect location in Europe for high availability. These two locations are named DX-A and DX-B, respectively. Each Region has a single AWS Transit Gateway that is configured to route all inter-VPC traffic within that Region.
Which solution will meet these requirements?

A. Create a transit VIF from the DX-A connection into a Direct Connect gateway. Create a transit VIF from the DX-B connection into the same Direct Connect gateway for high availability. Associate both the eu-west-1 and us-east-1 transit gateways with this Direct Connect gateway. Peer the transit gateways with each other to support cross-Region routing.
B. Create a private VIF from the DX-A connection into a Direct Connect gateway. Create a private VIF from the DX-B connection into the same Direct Connect gateway for high availability. Associate both the eu-west-1 and us-east-1 transit gateways with the Direct Connect gateway. Peer the transit gateways with each other to support cross-Region routing.
C. Create a transit VIF from the DX-A connection into a Direct Connect gateway. Associate the eu-west-1 transit gateway with this Direct Connect gateway. Create a transit VIF from the DX-B connection into a separate Direct Connect gateway. Associate the us-east-1 transit gateway with this separate Direct Connect gateway. Peer the Direct Connect gateways with each other to support high availability and cross-Region routing.
D. Create a transit VIF from the DX-A connection into a Direct Connect gateway. Create a transit VIF from the DX-B connection into the same Direct Connect gateway for high availability. Associate both the eu-west-1 and us-east-1 transit gateways with this Direct Connect gateway. Configure the Direct Connect gateway to route traffic between the transit gateways.

Answer: A

NEW QUESTION # 65
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