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A company uses a service to collect metadata from applications that the company hosts on premises. Consumer devices such as TVs and internet radios access the applications. Many older devices do not support certain HTTP headers and exhibit errors when these headers are present in responses. The company has configured an on-premises load balancer to remove the unsupported headers from responses sent to older devices, which the company identified by the User-Agent headers. The company wants to migrate the service to AWS, adopt serverless technologies, and retain the ability to support the older devices. The company has already migrated the applications into a set of AWS Lambda functions. Which solution will meet these requirements?
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A company has a serverless application comprised of Amazon CloudFront, Amazon API Gateway, and AWS Lambda functions. The current deployment process of the application code is to create a new version number of the Lambda function and run an AWS CLI script to update. If the new function version has errors, another CLI script reverts by deploying the previous working version of the function. The company would like to decrease the time to deploy new versions of the application logic provided by the Lambda functions, and also reduce the time to detect and revert when errors are identified. How can this be accomplished?

Question 102 - SAP-C02 discussion

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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?

A.
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.
Answers
A.
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.
B.
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.
Answers
B.
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.
C.
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.
Answers
C.
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.
D.
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.
Answers
D.
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.
Suggested answer: A

Explanation:

Connect the IoT sensors to AWS IoT 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 QuickSight for analysis. This solution meets the requirement of faster analysis and cost optimization by using AWS IoT Core to collect data from the IoT sensors in real-time and then using AWS Glue and Amazon Athena for efficient data analysis.

This solution involves connecting the loT sensors to the AWS loT Core, setting a rule to invoke an AWS Lambda function to parse the information, and saving a .csv file to Amazon S3. AWS Glue can be used to catalog the files and Amazon Athena and Amazon QuickSight can be used for analysis. This solution will enable faster and more cost-effective data analysis.

This solution is in line with the official Amazon Textbook and Resources for the AWS Certified Solutions Architect - Professional certification. In particular, the book states that: ''AWS IoT Core can be used to ingest and process the data, AWS Lambda can be used to process and transform the data, and Amazon S3 can be used to store the data. AWS Glue can be used to catalog and access the data, Amazon Athena can be used to query the data, and Amazon QuickSight can be used to visualize the data.'' (Source: https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS_Certified_Solutions_Architect_Professional_Exam_Guide_EN_v1.5.pdf)

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asked 16/09/2024
koffi .J N tsudze
33 questions
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