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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Handling outliers
- Data validation
- Data exploration (EDA)
- Statistical fundamentals at scale
- Encoding structured data types
- Visualization
- Feature selection
- Feature engineering
- Class imbalance
- Evaluation of data quality and feasibility
- Feature crosses
- Build data pipelines
- Design data pipelines
- Data privacy and compliance
- Database migration
- Batching and streaming data pipelines at scale
- Data ingestion
- Transformations (TensorFlow Transform)
- Data leakage and augmentation
- Streaming data (e.g. from IoT devices)
- Monitoring/changing deployed pipelines
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Handling missing data
- Managing large samples (TFRecords)
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
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Topics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:
- ML Pipeline Automation & Orchestration
- ML Model Development
- ML Problem Framing
- ML Solution Monitoring, Optimization, and Maintenance
- Data Preparation and Processing
- ML Solution Architecture
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Automate retraining and model updates |
| Topic 3: Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health |
| Topic 4: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Design and run experiments - Select appropriate model architectures and frameworks |
| Topic 5: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns |
| Topic 6: Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools |
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