Free Access Google.Professional-Machine-Learning-Engineer.v2024-09-13.q242 Practice Test (Page 37)

Question 176

Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?

A.The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

B.The model predicts videos as popular if the user who uploads them has over 10,000 likes.

C.The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.

D.The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.

Question 177

You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model's performance?

A.Number of messages flagged by the model per minute

B.Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

C.Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review

D.Number of messages flagged by the model per minute confirmed as being inappropriate by humans.

Question 178

You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?

A.Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.

B.Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.

C.Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.

D.Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.

Question 179

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

A.Ensure that feature expectations are captured in the schema

B.Ensure that all hyperparameters are tuned

C.Ensure that model performance is monitored

D.Ensure that training is reproducible

Question 180

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A.Use AutoML Natural Language to build the support requests classifier

B.Use an established text classification model on Al Platform to perform transfer learning

C.Use the Natural Language API to classify support requests

D.Use an established text classification model on Al Platform as-is to classify support requests

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