Exam Name: Google Professional Machine Learning Engineer
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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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:
- Data ingestion
- Encoding structured data types
- Monitoring/changing deployed pipelines
- Design data pipelines
- Batching and streaming data pipelines at scale
- Data privacy and compliance
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Class imbalance
- Database migration
- Feature crosses
- Statistical fundamentals at scale
- Data validation
- Feature engineering
- Data exploration (EDA)
- Handling outliers
- Data leakage and augmentation
- Build data pipelines
- Transformations (TensorFlow Transform)
- Streaming data (e.g. from IoT devices)
- Managing large samples (TFRecords)
- Visualization
- Handling missing data
- Feature selection
- Evaluation of data quality and feasibility
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
How much Professional Machine Learning Engineer - Google Cost
The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction |
| Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Design end-to-end ML workflows - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift |
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