You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's wait time for parts. You decided to focus on reduction of the 3 weeks aggregate reporting time.
Which modifications to the company's processes should you recommend?
To reduce the 3 weeks aggregate reporting time, it is essential to focus on both the efficiency of data transfer and the speed of data processing. Migrating from FTP to streaming transport will allow for immediate data transmission, thereby reducing the time lag associated with periodic uploads. Moving from CSV to a binary format will significantly enhance the speed of data processing because binary formats are generally faster to read and write compared to text-based formats like CSV. Additionally, developing machine learning analysis of metrics can provide predictive insights that help in reducing downtime further, although it is a secondary concern compared to improving data transfer and processing speeds. Increasing fleet cellular connectivity, as suggested in other options, might not be feasible due to cost and logistical constraints, and does not directly address the data processing delays leading to the three weeks of aggregate reporting time.
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.
There are 20 million TerramEarth vehicles in operation ... Approximately 200,000 have cellular connectivity. So, you're saying for them to keep cost low, increase cell phone bill from 0.01% connected to 80% connected? Statistical Analysis does not require such a large sample size. C CANNOT BE RIGHT.
It's B.
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.
from PCA samples
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.
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 two points of the introductory info referred: 1. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, Terram Earth collects a total of about 9 TB/day from these connected vehicles. 2. TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
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 .
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.
binary format makes faster bigquery write https://cloud.google.com/bigquery/docs/write-api#advantages
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
this question is from Goolge official PCA samples
ok for C
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
C is the right answer
C is right!
answer is B, binary is faster
Answer is B. C is wrong suggestion, think of cost and time for 80% cellular connection
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.
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.