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Operationalize ML Models
- 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);
- 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;
- 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.
For more info visit:
Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test
Prerequisites
There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems
The following will be discussed here:
- Validating a migration
- Monitoring pipelines
- Transformation
- Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
- Building and operationalizing pipelines
- Provisioning resources
- Batch and streaming
- Adjusting pipelines
- Building and operationalizing data processing systems
- Building and operationalizing storage systems
- Data acquisition and import
- Lifecycle management of data
- Testing and quality control
- Data cleansing
- Awareness of current state and how to migrate a design to a future state
- Building and operationalizing processing infrastructure
- Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
- Integrating with new data sources
- Storage costs and performance
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Topic 2: Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Topic 3: Ensuring solution quality | 28% | - Reliability and performance
|
| Topic 4: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
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