Exam Professional Machine Learning Engineer topic 1 question 12 discussion

ANS: C https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow: A user writes a ticket to Firebase, which triggers a Cloud Function. -The Cloud Function calls 3 different endpoints to enrich the ticket: -An AI Platform endpoint, where the function can predict the priority. -An AI Platform endpoint, where the function can predict the resolution time. -The Natural Language API to do sentiment analysis and word salience. -For each reply, the Cloud Function updates the Firebase real-time database. -The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

the answer should be C. The tickets do not include specific terms , which means, it doesn't need to be custom built. thus, we can use cloud NLP API instead of automl NLP.

ANS: C Tickets are not expected to have any domain-specific terms or jargon. Therefore we can use the Natural Language API, and we don't need to train our own model.

C) Eliminate A and D as not vision or images required. From B (Auto ML Natural Language) requires custom training and C) NLP API gives you sentiment analysis out of the box.

C - as Natural Language API has sentiment analysis and using the API over a custom model is always preferred

Req : serverless ML system + models to (predict ticket priority -predict ticket resolution time- perform sentiment analysis ) The proposed architecture has the following flow: A. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision. : No image data as input here. Only text (NLP) B. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language : Only sentiment for 3rd endpoint. No custom model needed : https://cloud.google.com/natural-language/automl/docs/beginners-guide . So autoML not required C. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API : 1- for classification(priority :high low medium), 2- ticket time-regression -3- sentiment analysis the CNL api is enough D. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API : No image data

Went with C

ANS: C This is the exact solution by Google: https://web.archive.org/web/20210618072649/https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture

ANS: B As you need to train custom regression models (Auto ML), as NLP API is not going to be able to rank your Priority and eval the Time.

ANS: C as NLP API is not able to perform custom Regression Models (predict time) and Priority. You need Auto ML o train your own

AI Platform (now Vertex AI) for both the predictions and Natural Language API for sentiment analysis since there are no specific terms (so no need to custom build something with an AutoML), so C

Correct answer is "C"

- by options eliminations A,D must be dropped we have no vision tasks in this system - answer between B,C , question stated "no specific domain or jargon" so natural laguage api is prefered over automl since there no custom entinites or custom training , so I vote for C

Community vote

Mine is C. Priority prediction is categorical. Resolution time is linear regression. Sentiment is a NLP problem.

Should be B, don't forget the domain specific terms and jargons https://medium.com/google-cloud/analyzing-sentiment-of-text-with-domain-specific-vocabulary-and-topics-726b8f287aef

not sure if I agree with b, I think D is a better choice