Course Overview
- Course Title: GCP Professional Machine Learning Engineer Practice Exams
- Instructor: Nico Wichmann (Nex Arc)
- Target Audience:
- ML engineers
- Data scientists
- Cloud architects
- Technical professionals with hands-on GCP ML experience
- Prerequisites:
- Practical experience with Google Cloud ML services
- Familiarity with Vertex AI, TensorFlow, AutoML, BigQuery ML
Curriculum Highlights
- Key Topics Covered:
- ML Problem Framing & Design (business-to-ML translation, approach selection)
- Data Engineering & Preparation (BigQuery, Dataflow, Cloud Storage pipelines)
- Model Building & Training (Vertex AI, TensorFlow, AutoML, BigQuery ML)
- ML Pipeline Automation (Vertex AI Pipelines, CI/CD, orchestration)
- Model Deployment & Serving (batch/real-time predictions, scaling)
- ML Solution Monitoring (drift detection, production maintenance)
- Key Skills Learned:
- Designing end-to-end ML workflows on Google Cloud
- Implementing MLOps pipelines with Vertex AI
- Deploying and scaling ML models for production
- Monitoring model performance and data drift
Course Format
- Duration:
- 6 full-length practice exams (~300+ questions total)
- Format:
- Self-paced online (timed simulations)
- Mobile-accessible
- Resources:
- Detailed answer explanations for all questions
- Performance tracking to identify weak areas
- Updated content reflecting latest GCP ML services
Additional Information
- Exam Alignment:
- Covers all official GCP Professional ML Engineer exam domains
- Matches real exam format and difficulty
- Instructor Credentials:
- Principal Cloud Architect with real-world GCP ML experience
- 4.7/5 instructor rating (282 reviews, 4,800+ students)