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The Google Professional-Machine-Learning-Engineer exam consists of multiple-choice and multiple-select questions, as well as case studies and hands-on labs. Professional-Machine-Learning-Engineer exam duration is two hours, and the passing score is 70%. Professional-Machine-Learning-Engineer exam fee is $200, and it can be taken remotely or at a testing center.

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Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their skills and knowledge in the field of machine learning. Professional-Machine-Learning-Engineer exam is designed to test the individual's ability to use Google Cloud Platform tools and services to create scalable and efficient machine learning models. Google Professional Machine Learning Engineer certification exam provides a credible and recognized way for professionals to demonstrate their expertise in the field of machine learning.

Google Professional Machine Learning Engineer Sample Questions (Q146-Q151):

NEW QUESTION # 146
You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

A. Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.
B. Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.
C. Use the Vertex Al ModelEvaluationOp component to evaluate the model.
D. Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

Answer: B

NEW QUESTION # 147
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

A. Store all ML metadata in Google Cloud's operations suite.
B. Manage your ML workflows with Vertex ML Metadata.
C. Manage all relational entities in the Hive Metastore.
D. Store your tf.logging data in BigQuery.

Answer: A

NEW QUESTION # 148
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A. Use an established text classification model on Al Platform to perform transfer learning
B. Use AutoML Natural Language to build the support requests classifier
C. Use an established text classification model on Al Platform as-is to classify support requests
D. Use the Natural Language API to classify support requests

Answer: A

Explanation:
the model cannot work as-is as the classes to predict will likely not be the same; we need to use transfer learning to retrain the last layer and adapt it to the classes we need

NEW QUESTION # 149
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
B. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
C. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
D. 1 Build a notification system on Firebase

Answer: D

Explanation:
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold Explanation:
Firebase is designed for exactly this sort of scenario. Also, it would not be possible to create millions of pubsub topics due to GCP quotas https://cloud.google.com/pubsub/quotas#quotas
https://firebase.google.com/docs/cloud-messaging

NEW QUESTION # 150
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
C. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
D. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Answer: D

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.html

NEW QUESTION # 151
......

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Google Professional Machine Learning Engineer Sample Questions (Q146-Q151):

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