Free Google Professional Machine Learning Engineer Exam Dumps

MultipleChoice

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?

A.

B.

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D.

MultipleChoice

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?

A Add a logging configuration to your application to emit logs to Cloud Logging.

MultipleChoice

You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

MultipleChoice

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 customers1 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?

MultipleChoice

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

A)

B)

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D)

MultipleChoice

You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

MultipleChoice

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?