GCP Data Architecture Test

This topic involves designing, orchestrating, and optimizing data pipelines that allow for the smooth flow of data between systems and across GCP services. This includes understanding Dataflow for stream and batch processing, Pub/Sub for message-based data movement, and Data Fusion for building ETL pipelines. Topics also cover pipeline performance tuning, error handling, and ensuring data consistency across sources.

Data governance focuses on establishing policies and practices to manage data availability, usability, integrity, and security. This topic covers Cloud IAM for role-based access control, Cloud DLP for data masking and encryption, and Data Catalog for metadata management. Additionally, it evaluates the implementation of regulatory compliance frameworks such as GDPR and HIPAA, and best practices in auditing and monitoring data usage in GCP.

GCP storage services provide flexible and scalable options for storing and managing structured, semi-structured, and unstructured data. This topic covers key services like BigQuery for large-scale analytics, Cloud SQL for relational databases, Firestore for NoSQL databases, and Cloud Storage for object storage. Questions will focus on understanding storage tiers, data lifecycle policies, partitioning strategies, and cost optimization techniques.

Streaming data solutions in GCP enable real-time data ingestion, processing, and analysis. This topic focuses on architecting streaming pipelines using Dataflow and Pub/Sub to process real-time data from various sources, such as IoT devices or transactional systems. It also covers designing fault-tolerant, scalable streaming architectures and ensuring low-latency processing, with attention to data ordering, windowing, and checkpointing for reliable stream processing.

Machine Learning in GCP involves designing and deploying ML models to production using services like Vertex AI, AutoML, and AI Platform. This topic includes building models for prediction, classification, and clustering, automating ML pipelines, and integrating these models with GCP data services for real-time predictions. Advanced concepts include model monitoring, feature engineering, model explainability, and scaling ML workflows using GCP infrastructure.

Data security in GCP ensures that data remains protected, both at rest and in transit. This topic covers encryption techniques (both managed by GCP and customer-supplied), secure key management using Cloud KMS, and ensuring proper access control with IAM policies. It also includes understanding Cloud Audit Logs for monitoring data access and actions, VPC Service Controls for securing access to data, and implementing compliance strategies for audits and certifications.

Architecting hybrid and multi-cloud data solutions is essential for organizations that require data integration between on-premises systems and multiple cloud providers. This topic covers strategies for building resilient, scalable, and performant architectures using Anthos to manage workloads across clouds. It includes understanding Google Cloud Transfer Service for migrating large datasets, Cloud Interconnect for secure network links, and managing data consistency across regions.

DevOps practices for data architectures ensure continuous integration and continuous deployment (CI/CD) of data pipelines. This topic focuses on using tools like Cloud Build, Cloud Source Repositories, and GitOps methodologies to implement CI/CD pipelines for data solutions. It includes automating infrastructure provisioning using Terraform and Deployment Manager, managing version control for data workflows, and ensuring pipeline reproducibility and scalability.

This topic covers the strategic planning and implementation of organization-wide data strategies, including designing Data Mesh architectures to enable decentralized data ownership. It also evaluates defining data governance frameworks, establishing self-service data platforms, and enabling data democratization for analytics teams. Topics include data stewardship, building a unified data fabric across the enterprise, and leveraging GCP services for holistic data strategy implementation.