Free Access Google.Professional-Machine-Learning-Engineer.v2024-09-13.q242 Practice Test (Page 43)

Question 206

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A.Train a regression model using AutoML Tables.

B.Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.

C.Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.

D.Develop a regression model using BigQuery ML.

Question 207

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 208

You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

A.This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

B.This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

C.This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

D.This is a good result because the accuracy across both groups is greater than 80%.

Correct Answer: B

This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:
* It indicates that the model is not learning from the data, but rather memorizing the majority class. Since
90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model's accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).
* It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model's accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model's accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model
* can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.
References:
* How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery
* Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery

Question 209

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

A.Use Al Platform for distributed training

B.Create a cluster on Dataproc for training

C.Create a Managed Instance Group with autoscaling

D.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Question 210

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 TensorFlow to build a custom medical entity extraction model.

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

C.Use the Healthcare Natural Language API to extract medical entities.

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

Other Version: 3498Google.Professional-Machine-Learning-Engineer.v2025-05-29.q260; 3187Google.Professional-Machine-Learning-Engineer.v2023-12-28.q120; 1381Google.Professional-Machine-Learning-Engineer.v2023-02-27.q25; 2873Google.Professional-Machine-Learning-Engineer.v2022-08-07.q66; 2396Google.Professional-Machine-Learning-Engineer.v2022-03-21.q70; 62Google.Prepawayete.Professional-Machine-Learning-Engineer.v2021-09-13.by.murray.54q.pdf

Latest Upload: 137Veeam.VMCE_v12.v2026-10-24.q119; 147API.API-510.v2026-10-24.q84; 123AIChE.CCPSC.v2026-10-24.q41; 162CIPS.L4M5.v2026-10-24.q263; 139InsuranceLicensing.InsNV_Health02.v2026-10-23.q44; 235Juniper.JN0-452.v2026-10-23.q141; 282EC-COUNCIL.312-38.v2026-10-22.q321; 149WGU.Health-Fitness-and-Wellness.v2026-10-21.q14; 180ISACA.COBIT-Design-and-Implementation.v2026-10-21.q57; 205NACE.NACE-CIP2-001.v2026-10-21.q36

Question 206

Question 207

Question 208

Question 209

Question 210

Download PDF File