Professional-Machine-Learning-Engineer Exam Dumps : Google Professional Machine Learning Engineer

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  Dat aproc 2  is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it.  You can also use Vertex AI Workbench 3 , a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Dataproc

Vertex AI Workbench

Google Professional Machine Learning Certification Exam 2023

Latest Google Profe ssional Machine Learning Engineer Actual Free Exam Questions

To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:

Use Vertex Explainable AI with the sampled Shapley method.  Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction 1 .  The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets 2 .  The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features 3 .

Enable Vertex AI Model Monitoring to check for feature distribution drift.  Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time 4 . Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.

 Comparing the training and evaluation losses of the current run is a good way to check if the model is overfitting or underfitting. If the losses are similar, it means that the model is generalizing well and can be deployed to a Vertex AI endpoint. Vertex AI endpoint is a service that allows you to serve your ML models online and scale them automatically. By using a training/serving skew threshold model monitoring, you can detect if there is a significant difference between the data used for training and the data used for serving. This can indicate that the model is becoming stale or inaccurate over time. When the model monitoring threshold is triggered, it means that the model needs to be retrained with the latest data. By redeploying the pipeline, you can automate the retraining process and update the model with the new data. This way, you can minimize the cost of retraining and ensure that your model is always up-to-date and accurate.  References :

Vertex AI documentation

Vertex AI endpoint documentation

Model monitoring documentation

Preparing for Google Clo ud Certification: Machine Learning Engineer Professional Certificate