Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect
that many features are non-informative. You want to remove the non-informative features from your model
while keeping the informative ones in their original form. Which technique should you use?
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench
environment for the members of your team and also limit access to other employees in your project. What
should you do?
You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?
You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
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