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:

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

Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

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

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

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

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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