Exam Professional Machine Learning Engineer topic 1 question 204 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 204
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts. Your team has assembled a set of annotated images from damage claim documents in the company’s database. The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to train models on Google Cloud. You need to quickly create an initial model. What should you do?

A. Download a pre-trained object detection model from TensorFlow Hub. Fine-tune the model in Vertex AI Workbench by using the annotated image data.
B. Train an object detection model in AutoML by using the annotated image data.
C. Create a pipeline in Vertex AI Pipelines and configure the AutoMLTrainingJobRunOp component to train a custom object detection model by using the annotated image data.
D. Train an object detection model in Vertex AI custom training by using the annotated image data.

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Suggested Answer: B 🗳️

by pikachu007 at Jan. 13, 2024, 4:34 a.m.

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louisaok

7 months ago

Selected Answer: B

>>" You have been given a sufficient budget to train models on Google Cloud" it is rare to see a company give enough money to run a mission-critical project.

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1 year, 5 months ago

Selected Answer: B

Speed: AutoML excels in creating high-quality models with minimal code and setup, significantly accelerating model development. Ease of use: It provides a user-friendly interface and automates many aspects of model training, making it accessible even for those without extensive ML expertise. Automatic optimization: AutoML automatically handles hyperparameter tuning, feature engineering, and architecture selection, reducing manual effort and expertise required. Custom object detection: It supports custom object detection tasks, directly addressing the need to identify damaged parts in images.

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