Exam Professional-Machine-Learning-Engineer Topic 1 Question 194 Discussion

Actual exam question for Google's Professional-Machine-Learning-Engineer exam
Question #: 194
Topic #: 1

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

A. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output. B. Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output. C. Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output. D. Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.

Suggested Answer: C Vote an answer

According to the official exam guide1, one of the skills assessed in the exam is to "explain the predictions of a trained model". TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

by Noel at May 08, 2025, 02:02 AM

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