Free Google Professional-Machine-Learning-Engineer Exam Questions

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

Page: 1 / 58

Total 289 Questions | Updated On: Mar 07, 2026

Add To Cart

Question 1

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?

A : 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.

B : 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.

C : 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.

D : 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.

Answer: A

Question 2

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

A : ply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.

B : ply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

C : n a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters.

D : n a hyperparameter tuning job on AI Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer: D

Question 3

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A : ee individual features: binned latitude, binned longitude, and one-hot encoded car type.

B : feature obtained as an element-wise product between latitude, longitude, and car type.

C : feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

D : o feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer: C

Question 4

You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

A : a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.

B : a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.

C : a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.

D : a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKEGive the report to the logistics team each month so they can fine-tune inventory levels.

Answer: B

Question 5

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A : crease the instance memory to 512 GB and increase the batch size.

B : place the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C : able early stopping in your Vertex AI Training job

D : able early stopping in your Vertex AI Training job

Answer: C

Page: 1 / 58

Total 289 Questions | Updated On: Mar 07, 2026

Add To Cart