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Google Professional-Machine-Learning-Engineer Exam Dumps Questions and Answers

Exam Code: Professional-Machine-Learning-Engineer
Vendor: Google
Last Update: Jan 31, 2023
Questions: 145
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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?



Use Data Catalog to search the BigQuery datasets by using keywords in the table description.


Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.


Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.


Execute a query in BigQuery to retrieve all the existing table names in your project using the

INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

Questions 2

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?



Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.


Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.


Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.


Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

Questions 3

Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?



Add synthetic training data where those phrases are used in non-toxic ways


Remove the model and replace it with human moderation.


Replace your model with a different text classifier.


Raise the threshold for comments to be considered toxic or harmful