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Last Updated: Jul 26, 2026

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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.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

  • Common sources of error (e.g., assumptions about data)
  • Use of edge compute
  • Deploying an ML pipeline
  • Distributed vs. single machine
  • ML APIs (e.g., Vision API, Speech API)
  • Continuous evaluation
  • Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
  • Conversational experiences (e.g., Dialogflow)
  • Measuring, monitoring, and troubleshooting machine learning models
  • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
  • Leveraging pre-built ML models as a service
  • Ingesting appropriate data
  • Choosing the appropriate training and serving infrastructure
  • Impact of dependencies of machine learning models
  • Operationalizing machine learning models
  • Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
  • Hardware accelerators (e.g., GPU, TPU)

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.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Streaming (e.g., windowing, late arriving data)
  • 2. AI data enrichment
  • 3. Data acquisition and import
  • 4. Integrating with new data sources
  • 5. Processing logic
  • 6. Batch processing
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
- Designing data processing resources
  • 1. Cost optimization
  • 2. Cluster sizing and autoscaling
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
Topic 2: Storing the data (~20% of the exam)20%- Using a data lake
  • 1. Processing data
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Monitoring the data lake
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Defining architecture to support data access patterns
  • 3. Mapping business requirements
  • 4. Deciding the degree of data normalization
Topic 3: Preparing and using data for analysis (~15% of the exam)15%- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
Topic 4: Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Planning for monitoring and alerting
  • 2. Recovering from failures
  • 3. Performing data quality and validation checks
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Workflow orchestration
  • 3. Scheduling jobs
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
Topic 5: Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Identity and Access Management (IAM)
  • 3. Data encryption
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
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