Free Access Google.Professional-Machine-Learning-Engineer.v2023-12-28.q120 Practice Test (Page 17)

Question 76

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A.1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map

B.1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction

C.1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

D.1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

Question 77

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?

A.User engagement as measured by the number of battles played daily per user

B.Average time players wait before being assigned to a team

C.Precision and recall of assigning players to teams based on their predicted versus actual ability

D.Rate of return as measured by additional revenue generated minus the cost of developing a new model

Question 78

A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

A.The learning rate should be increased because the optimization process was trapped at a local minimum.

B.The dropout rate at the flatten layer should be increased because the model is not generalized enough.

C.The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

D.The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Question 79

While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A.Increase the CPU load

B.Add caching to the pipeline

C.Add parallel interleave to the pipeline

D.Increase the network bandwidth

Question 80

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A.Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

B.Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

C.Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler

D.Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

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Question 76

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Question 78

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Question 80

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