Free Practice Questions for Google Professional Data Engineer Certification

Considerations include:

- Identity and Access Management (e.g., Cloud IAM and organization policies) - Data security (encryption and key management) - Privacy (e.g., strategies to handle personally identifiable information) - Regional considerations (data sovereignty) for data access and storage - Legal and regulatory compliance - Designing the project, dataset, and table architecture to ensure proper data governance - Multi-environment use cases (development vs. production)

Considerations include:

- Preparing and cleaning data (e.g., Dataform, Dataflow, and Cloud Data Fusion, prompting LLMs for query generation) - Monitoring and orchestration of data pipelines - Disaster recovery and fault tolerance - Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability - Data validation

Considerations include:

- Mapping current and future business requirements to the architecture - Designing for data and application portability (e.g., multi-cloud and data residency requirements) - Data staging, cataloging, profiling, and discovery (data governance)

Considerations include:

- Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state - Planning migration and validation to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)

Considerations include:

- Defining data sources and sinks - Defining data transformation and orchestration logic - Networking fundamentals - Data encryption

Considerations include:

- Data cleansing - Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka) - Transformations - Batch - Streaming (e.g., windowing, late arriving data) - Processing logic - AI data enrichment - Data acquisition and import - Integrating with new data sources

Considerations include:

- Job automation and orchestration (e.g., Cloud Composer and Workflows) - CI/CD (Continuous Integration and Continuous Deployment)

Considerations include:

- Analyzing data access patterns - Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore) - Planning for storage costs and performance - Lifecycle management of data

Considerations include:

- Designing the data model - Deciding the degree of data normalization - Mapping business requirements - Defining architecture to support data access patterns

Considerations include:

- Managing the lake (configuring data discovery, access, and cost controls) - Processing data - Monitoring the data lake

Considerations include:

- Building a data platform based on requirements by using Google Cloud tools (e.g., Dataplex, Dataplex Catalog, BigQuery, Cloud Storage) - Building a federated governance model for distributed data systems

Considerations include:

- Connecting to tools - Precalculating fields - BigQuery features for business intelligence (e.g., BI Engine, materialized views) - Troubleshooting poor performing queries - Security, data masking, Identity and Access Management (IAM), and Cloud Data Loss Prevention (Cloud DLP)

Considerations include:

- Preparing data for feature engineering, training and serving machine learning models (e.g., BigQueryML) - Preparing unstructured data for embeddings and retrieval-augmented generation (RAG)

Considerations include:

- Defining rules to share data - Publishing datasets - Publishing reports and visualizations - BigQuery sharing (Analytics Hub)

Considerations include:

- Minimizing costs per required business need for data - Ensuring that enough resources are available for business-critical data processes - Deciding between persistent or job-based data clusters (e.g., Dataproc)

Considerations include:

- Creating directed acyclic graphs (DAGs) for Cloud Composer - Scheduling and orchestrating jobs in a repeatable way

Considerations include:

- Capacity management (e.g., BigQuery Editions and reservations) - Interactive or batch query jobs

Considerations include:

- Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel) - Monitoring planned usage - Troubleshooting error messages, billing issues, and quotas - Manage workloads, such as jobs, queries, and compute capacity (reservations)

Considerations include:

- Designing system for fault tolerance and managing restarts - Running jobs in multiple regions or zones - Preparing for data corruption and missing data - Data replication and failover (e.g., Cloud SQL, Redis clusters)