Amazon Web Services crack4sure sap-c02 Actual Questions by Keegan q42 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Professional
Exam Code:	SAP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	625 Q&A's	Shared By:	keegan

Question 12

A company has purchased appliances from different vendors. The appliances all have loT sensors. The sensors send status information in the vendors' proprietary formats to a legacy application that parses the information into JSON. The parsing is simple, but each vendor has a unique format. Once daily, the application parses all the JSON records and stores the records in a relational database for analysis.

The company needs to design a new data analysis solution that can deliver faster and optimize costs.

Which solution will meet these requirements?

Options:

Connect the loT sensors to AWS loT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon S3. Use AWS Glue to catalog the files. Use Amazon Athena and Amazon OuickSight for analysis.

Migrate the application server to AWS Fargate, which will receive the information from loT sensors and parse the information into a relational format. Save the parsed information to Amazon Redshift for analysis.

Create an AWS Transfer for SFTP server. Update the loT sensor code to send the information as a .csv file through SFTP to the server. Use AWS Glue to catalog the files. Use Amazon Athena for analysis.

Use AWS Snowball Edge to collect data from the loT sensors directly to perform local analysis. Periodically collect the data into Amazon Redshift to perform global analysis.

Discussion

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Question 13

A company uses a load balancer to distribute traffic to Amazon EC2 instances in a single Availability Zone. The company is concerned about security and wants a solutions architect to re-architect the solution to meet the following requirements:

•Inbound requests must be filtered for common vulnerability attacks.

•Rejected requests must be sent to a third-party auditing application.

•All resources should be highly available.

Which solution meets these requirements?

Options:

Configure a Multi-AZ Auto Scaling group using the application's AMI. Create an Application Load Balancer (ALB) and select the previously created Auto Scaling group as the target. Use Amazon Inspector to monitor traffic to the ALB and EC2 instances. Create a web ACL in WAF. Create an AWS WAF using the web ACL and ALB. Use an AWS Lambda function to frequently push the Amazon Inspector report to the third-party auditing application.

Configure an Application Load Balancer (ALB) and add the EC2 instances as targets Create a web ACL in WAF. Create an AWS WAF using the web ACL and ALB name and enable logging with Amazon CloudWatch Logs. Use an AWS Lambda function to frequently push the logs to the third-party auditing application.

Configure an Application Load Balancer (ALB) along with a target group adding the EC2 instances as targets. Create an Amazon Kinesis Data Firehose with the destination of the third-party auditing application. Create a web ACL in WAF. Create an AWS WAF using the web ACL and ALB then enable logging by selecting the Kinesis Data Firehose as the destination. Subscribe to AWS Managed Rules in AWS Marketplace, choosing the WAF as the subscriber.<

Configure a Multi-AZ Auto Scaling group using the application's AMI. Create an Application Load Balancer (ALB) and select the previously created Auto Scaling group as the target. Create an Amazon Kinesis Data Firehose with a destination of the third-party auditing application. Create a web ACL in WAF. Create an AWS WAF using the WebACL and ALB then enable logging by selecting the Kinesis Data Firehose as the destination. Subscribe to AWS Ma

Discussion

Question 14

A company is building a solution in the AWS Cloud. Thousands or devices will connect to the solution and send data. Each device needs to be able to send and receive data in real time over the MQTT protocol. Each device must authenticate by using a unique X.509 certificate.

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

Options:

Set up AWS loT Core. For each device, create a corresponding Amazon MQ queue and provision a certificate. Connect each device to Amazon MQ.

Create a Network Load Balancer (NLB) and configure it with an AWS Lambda authorizer. Run an MQTT broker on Amazon EC2 instances in an Auto Scaling group. Set the Auto Scaling group as the target for the NLB. Connect each device to the NLB.

Set up AWS loT Core. For each device, create a corresponding AWS loT thing and provision a certificate. Connect each device to AWS loT Core.

Set up an Amazon API Gateway HTTP API and a Network Load Balancer (NLB). Create integration between API Gateway and the NLB. Configure a mutual TLS certificate authorizer on the HTTP API. Run an MQTT broker on an Amazon EC2 instance that the NLB targets. Connect each device to the NLB.

Discussion

Question 15

A company hosts an application on AWS. The application uses AWS Lambda functions that are invoked by an Amazon API Gateway API. The company has an Amazon CloudFront distribution that uses the API Gateway API as its origin. The CloudFront distribution serves web requests to customers worldwide.

During testing, users experienced slow responses from the application APIs. The company discovered that requests from different AWS Regions contained inconsistent query parameters with mixed-case letters, which caused increased cache misses and more requests to reach the Lambda functions.

The company wants to ensure that the API consistently provides responses with minimal latency.

Which solution will meet these requirements?

Options:

Create a new Lambda function to sort incoming request query parameters alphabetically and convert the parameters to lowercase. Configure the CloudFront distribution to use the Lambda@Edge function type. Configure the Lambda function to invoke on origin request.

Create a CloudFront function to sort incoming request query parameters alphabetically and convert the parameters to lowercase. Configure the CloudFront distribution to use the CloudFront Functions function type. Configure the CloudFront function to invoke on viewer request.

Configure the API Gateway API to use mapping templates to sort incoming request query parameters alphabetically and convert the parameters to lowercase before Lambda processes the request.

Configure the API Gateway API to use a Lambda authorizer to sort incoming request query parameters alphabetically and convert the parameters to lowercase before Lambda processes the request.

Discussion

Answer:

Explanation:

Comprehensive and Detailed Explanation From Exact Extract:

B is correct because the problem is caused by inconsistent query-string casing leading to CloudFront cache misses, and the best place to normalize the request is at the CloudFront edge before cache-key evaluation. CloudFront Functions run at the edge with very low latency and are intended for lightweight request manipulations (for example, normalizing URLs, headers, and query strings) on viewer request events. By sorting query parameters and converting them to lowercase on the viewer request, CloudFront will treat semantically equivalent requests as the same cache key, increasing cache hit ratio and reducing the number of origin calls to API Gateway and downstream Lambda invocations. This directly reduces latency for global users.

Why the other options are less suitable:

A (Lambda@Edge on origin request): Lambda@Edge can also modify requests, but it is heavier-weight than CloudFront Functions for simple normalization logic. Also, doing this on origin request occurs after CloudFront has already decided whether there is a cache hit. If the cache key is still based on the original mixed-case query string at the viewer side, you won’t consistently prevent cache misses. Normalization must happen before cache lookup to maximize cache hits.

C (API Gateway mapping templates): This normalizes the request only after it has already missed in CloudFront and reached the origin. It does not fix CloudFront cache fragmentation, so it does not meet the “minimal latency” goal as effectively.

D (Lambda authorizer): Lambda authorizers are for authorization/authentication decisions. Using an authorizer for query normalization adds unnecessary overhead and still happens at API Gateway (origin), not at CloudFront before cache evaluation.

[References:, Amazon CloudFront Documentation: cache behavior and cache keys; viewer request/origin request event flow, CloudFront Functions Documentation: designed for lightweight, high-scale, low-latency request/response manipulation at the edge; viewer request execution, AWS Lambda@Edge Documentation: edge compute for more complex logic; event timing differences (viewer vs origin), Amazon API Gateway Documentation: mapping templates and Lambda authorizers and their intended use cases, , ]

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