Google Professional Data Engineer (GCP) Practice Exam Test Set

Course Structure for Google Cloud Certified – Professional Data Engineer

Certified Professional Data Engineer analyzes data to gain insight into business outcomes, builds statistical models to support decision-making, and creates machine learning models to automate and simplify key business processes. The Google Cloud Certified – Professional Data Engineer exam assesses a candidates ability to –

1. Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

• Mapping storage systems to business requirements
• Data modeling
• Tradeoffs 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., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud 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

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, Cloud Datastore, Cloud Memorystore)
• Storage costs and performance
• Lifecycle 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

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 (Cloud Machine Learning Engine, 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)

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., Stackdriver)
• 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., Cloud 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., multi-cloud, data residency requirements)
• Data staging, cataloging, and discover