Exam Professional Machine Learning Engineer topic 1 question 276 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 276
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

A. Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
B. Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
C. Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.
D. Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.

Show Suggested Answer

Suggested Answer: B 🗳️

by Yan_X at Feb. 12, 2024, 9:02 a.m.

Comments

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omermahgoub

10 months ago

Selected Answer: A

Minimal Coding: BigQuery ML provides a user-friendly interface for training models, minimizing the need for extensive coding in tools like TensorFlow (C & D) Efficient Data Processing: Training directly in BigQuery eliminates data movement and leverages BigQuery's scalable infrastructure.

upvoted 1 times

omermahgoub

10 months ago

Matrix Factorization: This collaborative filtering technique is commonly used for recommender systems. BigQuery ML offers built-in support for matrix factorization, making it a good choice for your scenario.

upvoted 3 times

fitri001

10 months ago

it means you choose B?

upvoted 1 times

omermahgoub

9 months, 3 weeks ago

Yes, voted for A by mistake. The answer is B

upvoted 4 times

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vaibavi

11 months, 3 weeks ago

Selected Answer: B

least amount of coding--> BQML recommendations--> matrix factorization

upvoted 4 times

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guilhermebutzke

11 months, 4 weeks ago

Selected Answer: B

Using BigQuery ML for training a matrix factorization model would require less coding compared to building a custom model with TensorFlow in a Vertex AI Workbench notebook. BigQuery ML provides high-level APIs for machine learning tasks directly within the BigQuery environment, thus reducing the amount of coding needed for data preprocessing and model training. Matrix factorization is a commonly used technique for recommendation systems, making it a suitable choice for recommending new games to users based on their gameplay time data, user metadata, and game metadata.

upvoted 3 times

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Yan_X

1 year ago

Selected Answer: B

B https://developers.google.com/machine-learning/recommendation/collaborative/matrix

upvoted 3 times

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