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

Are you preparing for the Google Professional Machine Learning Engineer exam?
This course provides practice tests only — no video lectures. You’ll assess your readiness using 295 exam-style questions with detailed explanations for correct and incorrect options, plus links to the official Google Cloud documentation.

What’s included today:

• 295 unique, high-quality questions organized into full-length practice tests

• Explanations for every question (why the right answer is right, and the others aren’t)

• References to GCP/PML official docs so you can verify and deepen your understanding

• Question coverage aligned to the published exam objectives (e.g., data preparation, modeling, MLOps, responsible AI)

Note: This course does not include video lessons or hands-on labs. It’s purely for practice and self-assessment.

Sample style of question (illustrative):

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

• Instructor support for any clarification

• Detailed, well-referenced explanations

• Mobile-friendly with the Udemy app

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