Free Google Professional-Machine-Learning-Engineer Exam Questions

Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers

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Total 289 Questions | Updated On: Jan 12, 2026

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Question 1

You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

A : move training examples of high-performing subgroups, and retrain the model.

B : an additional objective to penalize the model more for errors made on the minority class, and retrain the model

C : move the features that have the highest correlations with the majority class.

D : ample or reweight your existing training data, and retrain the model

E : deploy the model, and provide a label explaining the model's behavior to users.

Answer: B,D

Question 2

You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?

A : Use the Healthcare Natural Language API to extract medical entities.

B : Use a BERT-based model to fine-tune a medical entity extraction model.

C : Use AutoML Entity Extraction to train a medical entity extraction model.

D : Use TensorFlow to build a custom medical entity extraction model.

Answer: B

Question 3

You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

A : mpare the loss performance for each model on a held-out dataset.

B : mpare the loss performance for each model on the validation data.

C : mpare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.

D : mpare the mean average precision across the models using the Continuous Evaluation feature.

Answer: B

Question 4

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

A : Train a model for object detection by using Vertex Al AutoML.

B : Train a model for image classification by using Vertex Al AutoML.

C : Develop the model training code for object detection and tram a model by using Vertex Al custom training.

D : Develop the model training code for image classification and train a model by using Vertex Al custom training

Answer: C

Question 5

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

A : Store features in Bigtable as key/value data.

B : Store features in Vertex Al Feature Store.

C : Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

D : Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: B

Page: 1 / 58

Total 289 Questions | Updated On: Jan 12, 2026

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