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: Mar 09, 2021

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Question 1

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 2

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?

A : 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.

B : eate a new view with BigQuery that does not include a column with city information

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

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

Answer: B

Question 3

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A : crease the size of the training batch.

B : crease the learning rate hyperparameter.

C : crease the learning rate hyperparameter.

Answer: C

Question 4

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

A : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

B : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

C : Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

D : Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Answer: D

Question 5

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

Page: 1 / 58

Total 289 Questions | Updated On: Mar 09, 2021

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