【評判】Practice Exams | Professional Machine Learning(Google GCP)

Design and implement robust data ingestion and preprocessing systems using Cloud Dataflow, BigQuery, and Cloud Storage
Build, train, evaluate, and fine-tune machine learning models with Vertex AI, AutoML, and custom training pipelines
Orchestrate and automate ML workflows using Vertex AI Pipelines and Kubeflow
Choose the right compute infrastructure (VMs, containers, GPUs, TPUs) for cost-effective and scalable ML model training and deployment
Implement best practices for data security, model governance, and responsible AI in production environments
Monitor deployed models with Vertex AI Model Monitoring and set up alerts for prediction drift and data skew
Apply effective strategies to address class imbalance, label noise, and data scarcity during model development
Translate business problems into ML solutions through effective problem framing, metric selection, and data analysis
Architect end-to-end ML systems that scale, adapt, and integrate well with production workloads on Google Cloud
Evaluate when and how to retrain models to ensure performance remains high in dynamic real-world conditions

Are you preparing for the Google Professional Machine Learning Engineer certification exam?

Welcome to the ultimate resource to assess your readiness with my expertly crafted practice exams.

This practice tests are designed to evaluate your ability to design, build, and productionize machine learning models using Google Cloud Platform, while following MLOps best practices and leveraging GCP’s AI/ML tools effectively.

Achieving this certification validates your skills in deploying scalable ML pipelines, understanding responsible AI principles, and applying the right tools—from model selection to infrastructure orchestration—for real-world applications.

Why does this matter? Because becoming a certified Machine Learning Engineer significantly boosts your career in data science and AI. Certified professionals are in high demand across industries aiming to transform data into intelligent action.

In this course, you'll find a comprehensive set of practice exams that blend foundational ML theory with advanced, real-world GCP implementation scenarios. Here's what you can expect:

• 299 unique, high-quality test questions

• Detailed explanations for both correct and incorrect answers

• Insights into GCP’s ML ecosystem, with references to official documentation

• Updated content reflecting the most recent GCP tools and MLOps practices

Covered services and topics include:
Vertex AI, BigQuery ML, AI Platform Pipelines, TensorFlow, AutoML, Feature Store, Explainable AI, Model Monitoring, and more.

This materials are curated to deepen your understanding and ensure your success with practical, exam-focused content.

Sample Question:

You are building a production ML pipeline to detect anomalies in transaction data. The dataset is updated daily in BigQuery, and you need to train a model regularly with minimal manual intervention. The model should be automatically retrained and deployed if the model's performance degrades.

What should you do?

A. Use a scheduled Cloud Function to export data from BigQuery to Cloud Storage and train a model on AI Platform using a custom container.

B. Use Vertex AI Pipelines with a scheduled trigger, incorporate a data validation and model evaluation step, and deploy only if model performance is above a threshold.

C. Manually run training jobs from the console whenever new data is available and deploy the model to a prediction endpoint if results look good.

D. Use AutoML Tables with scheduled retraining enabled and export predictions daily to BigQuery.

Explanation:

Incorrect Answers:

A: This approach adds unnecessary complexity. You don’t need Cloud Functions or custom containers when Vertex AI Pipelines provide built-in scheduling and orchestration.

C: Manual retraining does not scale and contradicts the requirement for automation and minimal manual intervention.

D: AutoML Tables does not support fine-grained control over pipeline steps such as evaluation gating or customized deployment logic.

Correct Answer:

B: Vertex AI Pipelines supports orchestration of ML workflows with scheduled triggers, evaluation steps, and conditional logic to automate retraining and deployment based on performance.

Join this course to master Google Cloud’s machine learning stack, gain hands-on experience, and confidently prepare for your certification.

Why choose?

• Retake the exams as often as needed

• Original and continuously updated question bank

• Instructor support for any clarification

• Detailed, well-referenced explanations

• Mobile-friendly with the Udemy app

• 30-day money-back guarantee if you’re not satisfied

I'm looking forward to helping you succeed. Happy learning, and best of luck on your Google Professional Machine Learning Engineer certification journey!