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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

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

• Define business success criteria
• Defining output use
• Defining business problems
• Assessing ML solution readiness
• Determination of when a model is deemed unsuccessful
• Key results
• Defining problem type (classification, regression, clustering, etc.)
• Identifying data sources
• Identifying nonML solutions
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Managing incorrect results
• Aligning with Google AI principles and practices (e.g. different biases)
• Assessing data readiness
• Success metrics
• Defining the input (features) and predicted output format
• Define ML problem
• Defining outcome of model predictions
• Assessing and communicating business impact

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

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

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

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:

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

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.