Google Cloud Professional Machine Learning Engineer — Question 206

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?

Answer options

• A. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
• B. Use scikit-learn 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.

Correct answer: C

Explanation

The correct answer is C because TensorFlow is well-suited for handling high-dimensional data like images, and Integrated Gradients provide a robust method for interpreting deep learning models by attributing output predictions to input features. Options A and B are not suitable as they rely on tree-based models, which may not perform as well with image data, while option D uses the sampled Shapley method, which is less effective than Integrated Gradients for deep learning models.