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Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:	Professional-Machine-Learning-Engineer
Available Languages:	Japanese, English
Exam Format:	Multiple choice, Multiple select
Related Certifications:	Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer
Real Exam Qty:	50-60
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Passing Score:	Not officially published, approximately 70%
Exam Price:	$200 USD (plus tax where applicable)
Recommended Training:	Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path Official Exam Guide
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:	Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/machine-learning-engineer

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Google Professional-Machine-Learning-Engineer Test Sample Questions - Professional-Machine-Learning-Engineer Reliable Test Prep

The second version is the web-based format of the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice test. Browsers such as Internet Explorer, Microsoft Edge, Firefox, Safari, and Chrome support the web-based practice exam. You don't have to install excessive plugins or software to attempt this Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice test.

Google Professional Machine Learning Engineer certification exam is a challenging but rewarding experience for professionals who are looking to advance their careers in the field of machine learning. By preparing thoroughly and demonstrating your proficiency in machine learning concepts and techniques, you can achieve this prestigious certification and open up new opportunities for your career.

Google Professional Machine Learning Engineer Sample Questions (Q398-Q403):

NEW QUESTION # 398
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

A. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
B. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
C. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
D. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Answer: C

NEW QUESTION # 399
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time.
User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

A. Average time players wait before being assigned to a team
B. User engagement as measured by the number of battles played daily per user
C. Precision and recall of assigning players to teams based on their predicted versus actual ability
D. Rate of return as measured by additional revenue generated minus the cost of developing a new model

Answer: B

NEW QUESTION # 400
Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics What should you do?

A. Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
B. Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.
C. Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.
D. Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.

Answer: B

NEW QUESTION # 401
You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

A. Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.
B. Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.
C. Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
D. Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Answer: B

NEW QUESTION # 402
You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

A. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.
B. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.
C. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.
D. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

Answer: A

Explanation:
Customer churn is a binary classification problem, where the target variable is whether a customer has churned or not. Therefore, a logistic regression model is more suitable than a linear regression model, which is used for regression problems. A logistic regression model can output the probability of a customer churning, which can be used to rank the customers by their churn risk and take appropriate actions1.
BigQuery ML is a service that allows you to create and execute machine learning models in BigQuery using standard SQL queries2. You can use BigQuery ML to create a logistic regression model for customer churn prediction by using the CREATE MODEL statement and specifying the LOGISTIC_REG model type3. You can use the historical customer data as the input table for the model, and specify the features and the label columns3.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models4. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases4. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction.
By registering the BigQuery ML model in Vertex AI Model Registry, you can leverage the Vertex AI features to evaluate and monitor the model performance4. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model's prediction.
The other options are not suitable for your scenario, because they either use the wrong model type, such as linear regression, or they do not use Vertex AI to evaluate the model performance, which would limit the insights and actions you can take based on the model results.
Reference:
Logistic Regression for Machine Learning
Introduction to BigQuery ML | Google Cloud
Creating a logistic regression model | BigQuery ML | Google Cloud
Introduction to Vertex AI Model Registry | Google Cloud
[Deploy a model to an endpoint | Vertex AI | Google Cloud]
[Vertex AI Experiments | Google Cloud]

NEW QUESTION # 403
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