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Amazon Web Services SAP-C02 Exam With Confidence Using Practice Dumps

Exam Code:
SAP-C02
Exam Name:
AWS Certified Solutions Architect - Professional
Certification:
AWS Certified Professional
Vendor:
Amazon Web Services
Questions:
674
Last Updated:
Jun 18, 2026
Exam Status:
Stable
Amazon Web Services SAP-C02

SAP-C02: AWS Certified Professional Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Solutions Architect - Professional Questions and Answers

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Question 1

A software-as-a-service (SaaS) provider exposes APIs through an Application Load Balancer (ALB). The ALB connects to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that is deployed in the us-east-I Region. The exposed APIs contain usage of a few non-standard REST methods: LINK, UNLINK, LOCK, and UNLOCK.

Users outside the United States are reporting long and inconsistent response times for these APIs. A solutions architect needs to resolve this problem with a solution that minimizes operational overhead.

Which solution meets these requirements?

Options:

A.

Add an Amazon CloudFront distribution. Configure the ALB as the origin.

B.

Add an Amazon API Gateway edge-optimized API endpoint to expose the APIs. Configure the ALB as the target.

C.

Add an accelerator in AWS Global Accelerator. Configure the ALB as the origin.

D.

Deploy the APIs to two additional AWS Regions: eu-west-l and ap-southeast-2. Add latency-based routing records in Amazon Route 53.

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Question 2

A company hosts a data-processing application on Amazon EC2 instances. The application polls an Amazon Elastic File System (Amazon EFS) file system for newly uploaded files. When a new file is detected, the application extracts data from the file and runs logic to select a Docker container image to process the file. The application starts the appropriate container image and passes the file location as a parameter.

The data processing that the container performs can take up to 2 hours. When the processing is complete, the code that runs inside the container writes the file back to Amazon EFS and exits.

The company needs to refactor the application to eliminate the EC2 instances that are running the containers

Which solution will meet these requirements?

Options:

A.

Create an Amazon Elastic Container Service (Amazon ECS) cluster. Configure the processing to run as AWS Fargate tasks. Extract the container selection logic to run as an Amazon EventBridge rule that starts the appropriate Fargate task. Configure the EventBridge rule to run when files are added to the EFS file system.

B.

Create an Amazon Elastic Container Service (Amazon ECS) cluster. Configure the processing to run as AWS Fargate tasks. Update and containerize the container selection logic to run as a Fargate service that starts the appropriate Fargate task. Configure an EFS event notification to invoke the Fargate service when files are added to the EFS file system.

C.

Create an Amazon Elastic Container Service (Amazon ECS) cluster. Configure the processing to run as AWS Fargate tasks. Extract the container selection logic to run as an AWS Lambda function that starts the appropriate Fargate task. Migrate the storage of file uploads to an Amazon S3 bucket. Update theprocessing code to use Amazon S3. Configure an S3 event notification to invoke the Lambda function when objects are created.

D.

Create AWS Lambda container images for the processing. Configure Lambda functions to use the container images. Extract the container selection logic torun as a decision Lambda function that invokes the appropriate Lambda processing function. Migrate the storage of file uploads to an Amazon S3 bucket.Update the processing code to use Amazon S3. Configure an S3 event notification to invoke the decision Lambda function when objects are created

Question 3

A company manufactures smart vehicles. The company uses a custom application to collect vehicle data. The vehicles use the MQTT protocol to connect to the application.

The company processes the data in 5-minute intervals. The company then copies vehicle telematics data to on-premises storage. Custom applications analyze this data to detect anomalies.

The number of vehicles that send data grows constantly. Newer vehicles generate high volumes of data. The on-premises storage solution is not able to scale for peak traffic, which results in data loss. The company must modernize the solution and migrate the solution to AWS to resolve the scaling challenges.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.

Use AWS IOT Greengrass to send the vehicle data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Create an Apache Kafka application to store the data in Amazon S3. Use a pretrained model in Amazon SageMaker to detect anomalies.

B.

Use AWS IOT Core to receive the vehicle data. Configure rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3. Create an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies.

C.

Use AWS IOT FleetWise to collect the vehicle data. Send the data to an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use the built-in machine learning transforms in AWS Glue to detect anomalies.

D.

Use Amazon MQ for RabbitMQ to collect the vehicle data. Send the data to an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use Amazon Lookout for Metrics to detect anomalies.

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