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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:
691
Last Updated:
Sep 13, 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 large company recently experienced an unexpected increase in Amazon RDS and Amazon DynamoDB costs. The company needs to increase visibility into details of AWS Billing and Cost Management There are various accounts associated with AWS Organizations, including many development and production accounts There is no consistent tagging strategy across the organization, but there are guidelines in place that require all infrastructure to be deployed using AWS CloudFormation with consistent tagging. Management requires cost center numbers and project ID numbers for all existing and future DynamoDB tables and RDS instances.

Which strategy should the solutions architect provide to meet these requirements?

Options:

A.

Use Tag Editor to tag existing resources Create cost allocation tags to define the cost center and project ID and allow 24 hours for tags to propagate to existing resources.

B.

Use an AWS Config rule to alert the finance team of untagged resources Create a centralized AWS Lambda based solution to tag untagged RDS databases and DynamoDB resources every hour using a cross-account role.

C.

Use Tag Editor to tag existing resources Create cost allocation tags to define the cost center and project ID Use SCPs to restrict resource creation that do not have the cost center and project ID on the resource.

D.

Create cost allocation tags to define the cost center and project ID and allow 24 hours for tags to propagate to existing resources Update existing federated roles to restrict privileges to provision resources that do not include the cost center and project ID on the resource.

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Question 2

A financial services company receives a regular data feed from its credit card servicing partner Approximately 5.000 records are sent every 15 minutes in plaintext, delivered over HTTPS directly into an Amazon S3 bucket with server-side encryption. This feed contains sensitive credit card primary account number (PAN) data The company needs to automatically mask the PAN before sending the data to another S3 bucket for additional internal processing. The company also needs to remove and merge specific fields, and then transform the record into JSON format Additionally, extra feeds are likely to be added in the future, so any design needs to be easily expandable.

Which solutions will meet these requirements?

Options:

A.

Trigger an AWS Lambda function on file delivery that extracts each record and writes it to an Amazon SQS queue. Trigger another Lambda function when new messages arrive in the SQS queue to process the records, writing the results to a temporary location in Amazon S3. Trigger a final Lambda function once the SQS queue is empty to transform the records into JSON format and send the results to another S3 bucket for internal processing.

B.

Trigger an AWS Lambda function on file delivery that extracts each record and writes it to an Amazon SQS queue. Configure an AWS Fargate container application to automatically scale to a single instance when the SQS queue contains messages. Have the application process each record, and transform the record into JSON format. When the queue is empty, send the results to another S3bucket for internal processing and scale down the AWS Fargate i

C.

Create an AWS Glue crawler and custom classifier based on the data feed formats and build a table definition to match. Trigger an AWS Lambda function on file delivery to start an AWS Glue ETL job to transform the entire record according to the processing and transformation requirements. Define the output format as JSON. Once complete, have the ETL job send the results to another S3 bucket for internal processing.

D.

Create an AWS Glue crawler and custom classifier based upon the data feed formats and build a table definition to match. Perform an Amazon Athena query on file delivery to start an Amazon EMR ETL job to transform the entire record according to the processing and transformation requirements. Define the output format as JSON. Once complete, send the results to another S3 bucket for internal processing and scale down the EMR cluster.

Question 3

A company is planning to migrate 1,000 on-premises servers to AWS. The servers run on several VMware clusters in the company’s data center. As part of the migration plan, the company wants to gather server metrics such as CPU details, RAM usage, operating system information, and running processes. The company then wants to query and analyze the data.

Which solution will meet these requirements?

Options:

A.

Deploy and configure the AWS Agentless Discovery Connector virtual appliance on the on-premises hosts. Configure Data Exploration in AWS Migration Hub. Use AWS Glue to perform an ETL job against the data. Query the data by using Amazon S3 Select.

B.

Export only the VM performance information from the on-premises hosts. Directly import the required data into AWS Migration Hub. Update any missing information in Migration Hub. Query the data by using Amazon QuickSight.

C.

Create a script to automatically gather the server information from the on-premises hosts. Use the AWS CLI to run the put-resource-attributes command to store the detailed server data in AWS Migration Hub. Query the data directly in the Migration Hub console.

D.

Deploy the AWS Application Discovery Agent to each on-premises server. Configure Data Exploration in AWS Migration Hub. Use Amazon Athena to run predefined queries against the data in Amazon S3.

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