Exam Professional Cloud Architect topic 8 question 5 discussion

C is right choice because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. C is correct because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

B. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics. Here's why: Migrating from FTP to streaming transport (e.g., using Google Cloud Pub/Sub) allows near real-time data transfer, significantly reducing the 3-week delay in reporting by enabling faster data collection and processing. Migrating from CSV to binary format improves data efficiency by reducing the size of the data payload, speeding up transfer and processing times. Developing machine learning analysis of metrics can help predict parts failures and optimize inventory management, further reducing downtime by ensuring that parts are available when needed.

b is slightly better than c because compreessed data will allow the pipe to be expanded. To Increase cell connectivity could overload the streaming process without the needed compression.

choosing B because it's gonna use Pub/Sub which is what Google wants in this case.

I don't C as a valid option, cause this might not depend on the company itself, but more on the client side, it will require a big investing and even maybe not possible because of signal reach to remote locations like fields outside of the cities. Option B focus on solving what the internal proceses first

PCA Samples A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. C is correct because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

C makes no sense, how are you going to improve cellular connectivity with anything Google has to offer? That's a local carrier thing. B is the answer.

this question is from Goolge official PCA samples

Answer is B. C is wrong suggestion, think of cost and time for 80% cellular connection

answer is B, binary is faster

I'm voting B in this one. My take on it is that increasing the cellular connectivity will generate high costs, and is not the main culprit of the 3 weeks delay, that is the problem we are trying to solve. There are two parts of the introductory info that are key We can say that the info we receive is quite fresh, 9TB a day. That makes increasing connectivity not so useful. And we also see that the main culprit here is the ETL process. Which would be solved migrating to streaming and handling binary format instead of FTP with CSVs.

The most effective way to reduce the 3 weeks aggregate reporting time and achieve the business requirement of reducing downtime would be to migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics. This would significantly reduce the time it takes to collect and analyze data

binary format makes faster bigquery write https://cloud.google.com/bigquery/docs/write-api#advantages

A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. C is correct because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

This question is in the sample questions from google A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. C is correct because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

go for B must go for streaming and faster processing (scalability on binary format) option C makes no sense as there is no vehicle connectivity problem mentioned (no need to change cellular network)- delay is after data is already received .