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Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Machine-Learning-Engineer
Exam Name:
Google Professional Machine Learning Engineer
Certification:
Machine Learning Engineer
Vendor:
Google
Questions:
296
Last Updated:
Apr 22, 2026
Exam Status:
Stable
Google Professional-Machine-Learning-Engineer

Professional-Machine-Learning-Engineer: Machine Learning Engineer Exam 2025 Study Guide Pdf and Test Engine

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Google Professional Machine Learning Engineer Questions and Answers

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Question 1

You work for a multinational organization that has recently begun operations in Spain. Teams within your organization will need to work with various Spanish documents, such as business, legal, and financial documents. You want to use machine learning to help your organization get accurate translations quickly and with the least effort. Your organization does not require domain-specific terms or jargon. What should you do?

Options:

A.

Create a Vertex Al Workbench notebook instance. In the notebook, convert the Spanish documents into plain text, and create a custom TensorFlow seq2seq translation model.

B.

Create a Vertex Al Workbench notebook instance. In the notebook, extract sentences from the documents, and train a custom AutoML text model.

C.

Use Google Translate to translate 1.000 phrases from Spanish to English. Using these translated pairs, train a custom AutoML Translation model.

D.

Use the Document Translation feature of the Cloud Translation API to translate the documents.

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Question 2

You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization ' s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

Options:

A.

Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

B.

Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.

C.

Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations

D.

Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

Question 3

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine- ' battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

Options:

A.

Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.

B.

Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.

C.

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.

D.

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

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