Expert Answers to Google Exam Professional-Machine-Learning-Engineer Questions

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Machine Learning Engineer Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

Last Update May 2, 2026
Total Questions : 296

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Questions 2

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

Options:

Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

Discussion 0

Questions 3

You are building a linear regression model on BigQuery ML to predict a customer ' s likelihood of purchasing your company ' s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

Options:

Create a new view with BigQuery that does not include a column with city information

Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.

Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

Discussion 0

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Questions 4

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

Options:

Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient " and cookware " and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model’s performance on a holdout dataset.

Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model ' s performance on a prelabeled dataset.

Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Discussion 0

Questions 5

You are developing an ML model in a Vertex Al Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

Options:

1 Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.

2 After a successful experiment create a Vertex Al pipeline.

1. Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket and upload the models to Vertex Al Model Registry.

2 After a successful experiment create a Vertex Al pipeline.

1 Create a Vertex Al pipeline with parameters you want to track as arguments to your Pipeline Job Use the Metrics. Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.

2. Associate the pipeline with your experiment when you submit the job.

1 Create a Vertex Al pipeline Use the Dataset and Model artifact types from the Kubeflow Pipelines. DSL as the inputs and outputs of the components in your pipeline.

2. In your training component use the Vertex Al SDK to create an experiment run Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.

Discussion 0

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