Google Professional-Machine-Learning-Engineer : Google Professional Machine Learning Engineer

Your team is building an application for a global bank that will be used by millions of customers.
You built a forecasting model that predicts customers' account balances 3 days in the future.
Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A. 1. Create a Pub/Sub topic for each user.
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• B. 1. Build a notification system on Firebase.
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• C. 1. Build a notification system on Firebase.
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.
• D. 1. Create a Pub/Sub topic for each user.
  2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?

• A. Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
• B. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
• C. Use Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
• D. Use Cloud Composer to build the training pipelines for custom deep learning-based models

Your research team runs large-scale reinforcement learning experiments that require distributing Python workloads across hundreds of nodes with dynamic task scheduling and shared object storage between workers. The team already uses Ray locally and wants to keep the same APIs.
You need a managed way to run these workloads and to reuse the same cluster for interactive development. What should you do?

• A. Package each experiment as an independent custom training job, and submit hundreds of jobs in parallel.
• B. Create a Ray cluster on Agent Platform, connect the Workbench development environment to it, and submit Ray jobs to the cluster.
• C. Use Dataflow with a custom DoFn that wraps each reinforcement learning worker.
• D. Deploy a Dataproc cluster with Spark, and rewrite the experiments using Spark RDDs.

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?

• A. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
• B. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
• C. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
• D. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

• A. Store features in Vertex AI Feature Store.
• B. Store features in Bigtable as key/value data.
• C. Store features as a Vertex AI dataset, and use those features to train the models hosted in Vertex AI endpoints.
• D. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.