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.

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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);
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;

Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

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

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates
Topic 2: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads
Topic 3: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Transforming and cleaning data 2. Ingesting data from various sources 3. Orchestrating data workflows - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Topic 4: Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Topic 5: Designing data processing systems	20%	- Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance