Google Cloud Certified Professional Data Engineer

Designing data processing systemsSelecting the appropriate storage technologies. Considerations include:● Mapping storage systems to business requirements● Data modeling● Trade-offs involving latency, throughput, transactions● Distributed systems● Schema designDesigning data pipelines. Considerations include:● Data publishing and visualization (e.g., BigQuery)● Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)● Online (interactive) vs.

batch predictions● Job automation and orchestration (e.g., Cloud Composer)Designing a data processing solution. Considerations include:● Choice of infrastructure● System availability and fault tolerance● Use of distributed systems● Capacity planning● Hybrid cloud and edge computing● Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)● At least once, in-order, and exactly once, etc., event processingMigrating data warehousing and data processing. Considerations include:● Awareness of current state and how to migrate a design to a future state● Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)● Validating a migrationBuilding and operationalizing data processing systemsBuilding and operationalizing storage systems.

Considerations include:● Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)● Storage costs and performance● Life cycle management of dataBuilding and operationalizing pipelines. Considerations include:● Data cleansing● Batch and streaming● Transformation● Data acquisition and import● Integrating with new data sourcesBuilding and operationalizing processing infrastructure. Considerations include:● Provisioning resources● Monitoring pipelines● Adjusting pipelines● Testing and quality controlOperationalizing machine learning modelsLeveraging pre-built ML models as a service.

Considerations include:● ML APIs (e.g., Vision API, Speech API)● Customizing ML APIs (e.g., AutoML Vision, Auto ML text)● Conversational experiences (e.g., Dialogflow)Deploying an ML pipeline. Considerations include:● Ingesting appropriate data● Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)● Continuous evaluationChoosing the appropriate training and serving infrastructure. Considerations include:● Distributed vs.

single machine● Use of edge compute● Hardware accelerators (e.g., GPU, TPU)Measuring, monitoring, and troubleshooting machine learning models. Considerations include:● Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)● Impact of dependencies of machine learning models● Common sources of error (e.g., assumptions about data)Ensuring solution qualityDesigning for security and compliance. Considerations include:● Identity and access management (e.g., Cloud IAM)● Data security (encryption, key management)● Ensuring privacy (e.g., Data Loss Prevention API)● Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))Ensuring scalability and efficiency.

Considerations include:● Building and running test suites● Pipeline monitoring (e.g., Cloud Monitoring)● Assessing, troubleshooting, and improving data representations and data processing infrastructure● Resizing and autoscaling resourcesEnsuring reliability and fidelity. Considerations include:● Performing data preparation and quality control (e.g., Dataprep)● Verification and monitoring● Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)● Choosing between ACID, idempotent, eventually consistent requirementsEnsuring flexibility and portability. Considerations include:● Mapping to current and future business requirements● Designing for data and application portability (e.g., multicloud, data residency requirements)● Data staging, cataloging, and discovery