Free Access Google.Professional-Machine-Learning-Engineer.v2025-05-29.q260 Practice Test (Page 48)

Question 231

You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A.Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.

B.Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

C.Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

D.Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew.

Question 232

You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort. What should you do first?

A.Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.

B.Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.

C.Create a Vertex Al Workbench notebook with instance type n2-standard-4.

D.Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.

Question 233

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A.Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

B.Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

C.Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

D.Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

Question 234

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

A.Regression classification

B.Binary classification

C.Multi-category classification

D.Streaming classification

Question 235

Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

A.Configure a Cloud Build trigger with the event set as "Pull Request"

B.Configure a Cloud Build trigger with the event set as "Push to a branch"

C.Configure a Cloud Function that builds the repository each time there is a code change.

D.Configure a Cloud Function that builds the repository each time a new branch is created.

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