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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems
The following will be discussed here:
- Hybrid cloud and edge computing
- Data modeling
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Designing data processing systems
- Tradeoffs involving latency, throughput, transactions
- System availability and fault tolerance
- Mapping storage systems to business requirements
- Selecting the appropriate storage technologies
- Schema design
- Job automation and orchestration (e.g., Cloud Composer)
- Data publishing and visualization (e.g., BigQuery)
- Online (interactive) vs. batch predictions
- Designing data pipelines
- Capacity planning
- At least once, in-order, and exactly once, etc., event processing
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Distributed systems
- Choice of infrastructure
- Use of distributed systems
Target Audience
The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
Who is the Professional Data Engineer Exam Intended for?
This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models
The following will be discussed here:
- Measuring, monitoring, and troubleshooting machine learning models
- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
- Leveraging pre-built ML models as a service
- Use of edge compute
- Choosing the appropriate training and serving infrastructure
- Operationalizing machine learning models
- Common sources of error (e.g., assumptions about data)
- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
- Hardware accelerators (e.g., GPU, TPU)
- Impact of dependencies of machine learning models
- Deploying an ML pipeline
- ML APIs (e.g., Vision API, Speech API)
- Ingesting appropriate data
- Distributed vs. single machine
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
- Continuous evaluation
- Conversational experiences (e.g., Dialogflow)
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Overview:
| Certification Vendor: | Google Cloud |
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Exam Price: | USD 200 (plus tax where applicable) |
| Exam Format: | Multiple choice, Multiple select |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Available Languages: | English, Japanese |
| Real Exam Qty: | 40 - 50 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Passing Score: | 700 / 1000 |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer日本語 Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Designing data processing systems | 20% | - Designing for business requirements
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
By Atwood
