Google Professional Machine Learning Engineer Google Professional Machine Learning Engineer Online Training
Google Professional Machine Learning Engineer Online Training
The questions for Professional Machine Learning Engineer were last updated at Feb 16,2025.
- Exam Code: Professional Machine Learning Engineer
- Exam Name: Google Professional Machine Learning Engineer
- Certification Provider: Google
- Latest update: Feb 16,2025
You are designing an architecture with a serverless 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?
- A . 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
- B . 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API
- C . 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
- D . 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE).
How should you architect this workflow?
- A . Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- B . Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- C . Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- D . Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud.
What should you do?
- A . Use Vertex Al Platform for distributed training
- B . Create a cluster on Dataproc for training
- C . Create a Managed Instance Group with autoscaling
- D . Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
You work for a large technology company that wants to modernize their contact center. You have
been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time.
How should you build the model?
- A . Use the Al Platform Training built-in algorithms to create a custom model
- B . Use AutoML Natural Language to extract custom entities for classification
- C . Use the Cloud Natural Language API to extract custom entities for classification
- D . Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers.
What factors should you consider before building the model?
- A . Redaction, reproducibility, and explainability
- B . Traceability, reproducibility, and explainability
- C . Federated learning, reproducibility, and explainability
- D . Differential privacy federated learning, and explainability
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns.
How should you ensure that AutoML fits the best model to your data?
- A . Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately
Choose an automatic data split across the training, validation, and testing sets - B . Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate
transformations Choose an automatic data split across the training, validation, and testing sets - C . Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- D . Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns.
How should you ensure that AutoML fits the best model to your data?
- A . Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately
Choose an automatic data split across the training, validation, and testing sets - B . Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate
transformations Choose an automatic data split across the training, validation, and testing sets - C . Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- D . Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns.
How should you ensure that AutoML fits the best model to your data?
- A . Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately
Choose an automatic data split across the training, validation, and testing sets - B . Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate
transformations Choose an automatic data split across the training, validation, and testing sets - C . Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- D . Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns.
How should you ensure that AutoML fits the best model to your data?
- A . Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately
Choose an automatic data split across the training, validation, and testing sets - B . Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate
transformations Choose an automatic data split across the training, validation, and testing sets - C . Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- D . Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold
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