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Preparation Process
The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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:
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Visualization
- Build data pipelines
- Data validation
- Transformations (TensorFlow Transform)
- Data leakage and augmentation
- Database migration
- Class imbalance
- Evaluation of data quality and feasibility
- Data exploration (EDA)
- Encoding structured data types
- Streaming data (e.g. from IoT devices)
- Handling missing data
- Design data pipelines
- Handling outliers
- Monitoring/changing deployed pipelines
- Feature selection
- Statistical fundamentals at scale
- Data ingestion
- Batching and streaming data pipelines at scale
- Feature engineering
- Feature crosses
- Data privacy and compliance
- Managing large samples (TFRecords)
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What is the duration, language, and format of Professional Machine Learning Engineer - Google
- No negative marking for wrong answers
- Language of Exam: English, Japanese, Korean
- Type of Questions: Multiple choice (MCQs), multiple answers
- Duration of Exam: 120 minutes
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| Topic 2: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 3: Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Topic 4: Scaling prototypes into ML models | - Hyperparameter tuning - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 5: Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving |
| Topic 6: Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
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