Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

Google Professional Machine Learning Engineer Exam - Topic 9 Question 36 Discussion

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?
D) Deploy the model on Al Platform and create a version of it for online inference.
A) Use the batch prediction functionality of Al Platform
B) Create a serving pipeline in Compute Engine for prediction
C) Use Cloud Functions for prediction each time a new data point is ingested

Google Professional Machine Learning Engineer Exam - Topic 9 Question 36 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 36
Topic #: 9
[All Professional Machine Learning Engineer Questions]

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: D

by Rebecka at May 04, 2022, 11:20 PM

Limited Time Offer

25%

Off

Get Premium Professional Machine Learning Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

0/2000 characters
Submit Cancel
Quentin
10 months ago
Wait, are we sure A is the most efficient?
upvoted 0 times
...
Garry
10 months ago
D seems like a solid option for real-time needs!
upvoted 0 times
...
Deandrea
11 months ago
C sounds interesting, but isn't that too manual?
upvoted 0 times
...
Sophia
11 months ago
I think B could work too, but it might be overkill.
upvoted 0 times
...
Coral
11 months ago
A is the best choice for batch processing!
upvoted 0 times
...
Carin
11 months ago
Deploying the model for online inference sounds interesting, but I wonder if it’s necessary for daily batch processing. I think batch prediction might be simpler.
upvoted 0 times
...
Sommer
11 months ago
I feel like creating a serving pipeline in Compute Engine could work, but it might require more setup than just using the AI Platform's batch prediction.
upvoted 0 times
...
Myra
11 months ago
I'm not entirely sure, but I think using Cloud Functions might be more suited for real-time predictions rather than batch processing.
upvoted 0 times
...
Karl
11 months ago
I remember we discussed batch prediction in our last study session. It seems like a good fit for processing daily aggregated data without much manual work.
upvoted 0 times
...
Johana
11 months ago
Hmm, I'm a bit confused on this one. I'm not sure if the purpose is to underpin the prioritization of requirements or to position DSDM projects for a successful outcome. I'll have to think this through more carefully.
upvoted 0 times
...
Walker
11 months ago
Okay, I think the key here is to focus on accelerating the IP ramp-up process. If they can migrate to pre-warmed IP addresses, that could help them get up and running faster.
upvoted 0 times
...

Save Cancel