How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

• Design data processing systems
• Ensure solution quality
• Build and operationalize data processing systems
• Operationalize machine learning models

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

Reference: https://cloud.google.com/certification/data-engineer

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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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Operationalize ML Models

• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);

Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

• Storage costs and performance
• Building and operationalizing data processing systems
• Adjusting pipelines
• Integrating with new data sources
• Awareness of current state and how to migrate a design to a future state
• Validating a migration
• Batch and streaming
• Lifecycle management of data
• Testing and quality control
• Transformation
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Data acquisition and import
• Building and operationalizing storage systems
• Building and operationalizing pipelines
• Building and operationalizing processing infrastructure
• Monitoring pipelines
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Data cleansing
• Provisioning resources