Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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

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:

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

How to Prepare For Professional Machine Learning Engineer - Google

Preparation Guide for Professional Machine Learning Engineer - Google

Introduction for Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.

The Professional Machine Learning Engineer exam assesses your ability to:

• Prepare and process data
• Automate & orchestrate ML pipelines
• Frame ML problems
• Architect ML solutions
• Develop ML models
• Monitor, optimize, and maintain ML solutions

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

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