Free Access Google.Professional-Machine-Learning-Engineer.v2023-12-28.q120 Practice Test (Page 20)

Question 91

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 dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

A.Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

B.Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

C.Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

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

Question 92

You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

A.Change the learning rate

B.Reduce the image shape

C.Change the optimizer

D.Reduce the batch size

Question 93

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A.Configure AutoML Tables to perform the classification task

B.Run a BigQuery ML task to perform logistic regression for the classification

C.Use Al Platform Notebooks to run the classification model with pandas library

D.Use Al Platform to run the classification model job configured for hyperparameter tuning

Question 94

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

A.Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.

B.Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.

C.Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.

D.Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Question 95

You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

A.Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.

B.Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.

C.Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.

D.Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

Other Version: 3513Google.Professional-Machine-Learning-Engineer.v2025-05-29.q260; 4735Google.Professional-Machine-Learning-Engineer.v2024-09-13.q242; 1384Google.Professional-Machine-Learning-Engineer.v2023-02-27.q25; 2875Google.Professional-Machine-Learning-Engineer.v2022-08-07.q66; 2399Google.Professional-Machine-Learning-Engineer.v2022-03-21.q70; 62Google.Prepawayete.Professional-Machine-Learning-Engineer.v2021-09-13.by.murray.54q.pdf

Latest Upload: 134Salesforce.Strategy-Designer.v2026-10-26.q75; 213Veeam.VMCE_v12.v2026-10-24.q119; 210API.API-510.v2026-10-24.q84; 163AIChE.CCPSC.v2026-10-24.q41; 225CIPS.L4M5.v2026-10-24.q263; 157InsuranceLicensing.InsNV_Health02.v2026-10-23.q44; 284Juniper.JN0-452.v2026-10-23.q141; 320EC-COUNCIL.312-38.v2026-10-22.q321; 161WGU.Health-Fitness-and-Wellness.v2026-10-21.q14; 224ISACA.COBIT-Design-and-Implementation.v2026-10-21.q57

Question 91

Question 92

Question 93

Question 94

Question 95

Download PDF File