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Professional Data Engineer

Certification exam guide

A Professional Data Engineer enables data-driven decision-making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

Section 1: Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

    ●  Mapping storage systems to business requirements

    ●  Data modeling

    ●  Trade-offs involving latency, throughput, transactions

    ●  Distributed systems

    ●  Schema design

1.2 Designing 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)

1.3 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 processing

1.4 Migrating 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 migration

Section 2: Building and operationalizing data processing systems

2.1 Building 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 data

2.2 Building and operationalizing pipelines. Considerations include:

    ●  Data cleansing

    ●  Batch and streaming

    ●  Transformation

    ●  Data acquisition and import

    ●  Integrating with new data sources

2.3 Building and operationalizing processing infrastructure. Considerations include:

    ●  Provisioning resources

    ●  Monitoring pipelines

    ●  Adjusting pipelines

    ●  Testing and quality control

Section 3: Operationalizing machine learning models

3.1 Leveraging 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)

3.2 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 evaluation

3.3 Choosing the appropriate training and serving infrastructure. Considerations include:

    ●  Distributed vs. single machine

    ●  Use of edge compute

    ●  Hardware accelerators (e.g., GPU, TPU)

3.4 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)

Section 4: Ensuring solution quality

4.1 Designing 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))

4.2 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 resources

4.3 Ensuring 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 requirements

4.4 Ensuring 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