What is the duration, language, and format of Professional Machine Learning Engineer - Google

• Type of Questions: Multiple choice (MCQs), multiple answers
• Language of Exam: English, Japanese, Korean
• Duration of Exam: 120 minutes
• No negative marking for wrong answers

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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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)
• Data privacy and compliance
• Statistical fundamentals at scale
• Batching and streaming data pipelines at scale
• Database migration
• Feature crosses
• Managing large samples (TFRecords)
• Design data pipelines
• Data leakage and augmentation
• Handling outliers
• Data ingestion
• Build data pipelines
• Handling missing data
• Transformations (TensorFlow Transform)
• Feature selection
• Data validation
• Monitoring/changing deployed pipelines
• Class imbalance
• Data exploration (EDA)
• Encoding structured data types
• Streaming data (e.g. from IoT devices)
• Visualization
• Feature engineering
• Evaluation of data quality and feasibility

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Distributed training
• Model performance against baselines, simpler models, and across the time dimension
• Overfitting
• Modeling techniques given interpretability requirements
• Model explainability on Cloud AI Platform
• Choice of framework and model
• Model generalization
• Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
• Tracking metrics during training
• Transfer learning
• Retraining/redeployment evaluation
• Unit tests for model training and serving
• Hardware accelerators
• Scale model training and serving
• Training a model as a job in different environments
• Productionizing
• Build a model

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