Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 22

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error 'Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

You work at an ecommerce startup. You need to create a customer churn prediction model Your company's recent sales records are stored in a BigQuery table You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost How should you build your first model?

You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps

1. Randomly split the data into training and evaluation datasets in a 65/35 ratio

2. Conduct feature engineering

3 Obtain metrics for the evaluation dataset.

4 Compare models trained in different pipeline executions

How should you execute these steps'?