Sample Questions from Professional Data Engineer - Google Cloud (PDE)

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to migrate a large-scale monolithic application to Google Cloud. The application has multiple interdependent services. What approach should they take to ensure minimal downtime and maintain service integrity during the migration?

   • Implement a blue-green deployment strategy using Google Kubernetes Engine (GKE) to run both the old and new versions of the application simultaneously, gradually shifting traffic to the new version.
   • Use Google Cloud's Dataflow to stream data from the old application to the new one, ensuring data consistency and minimizing downtime.
   • Migrate the application in a single, large-scale operation using Google Cloud's Deployment Manager to orchestrate the entire process.
   • Adopt a microservices architecture from the start, breaking down the monolithic application into smaller, independently deployable services using Cloud Functions.

2. Consider the situation where a data engineering team is tasked with optimizing the performance of a data pipeline that processes terabytes of log data daily. Which method solves it best?

   • Utilize Google Cloud's BigQuery for data storage and processing, leveraging its built-in optimization features and partitioning strategies to enhance query performance.
   • Implement a custom ETL process using Apache Beam on Google Cloud Dataflow, optimizing the pipeline for parallel processing and minimizing data shuffling.
   • Store the log data in Google Cloud Storage and use Google Cloud's Dataproc to run Apache Spark jobs for data processing, fine-tuning the Spark configurations for optimal performance.
   • Deploy a real-time streaming pipeline using Google Cloud Pub/Sub and Cloud Dataflow, processing data as it arrives to reduce latency and improve overall pipeline efficiency.

3. Which option best evaluates the trade-offs between using Google Cloud Storage vs. Google Cloud Filestore for hosting a shared file system for a collaborative data science team?

   • Google Cloud Storage offers high durability and scalability with object storage, suitable for long-term data archiving and infrequent access, whereas Google Cloud Filestore provides a fully managed Network File System (NFS) for high-performance, low-latency file sharing, ideal for active collaboration and frequent data access.
   • Google Cloud Storage is optimized for serving static website content and media files, while Google Cloud Filestore is designed for dynamic content and real-time file sharing, making it the better choice for collaborative data science workloads.
   • Google Cloud Storage integrates seamlessly with Google Cloud's data analytics services, whereas Google Cloud Filestore is better suited for general-purpose file sharing and does not offer the same level of integration with analytics tools.
   • Google Cloud Storage is more cost-effective for small-scale data storage needs, while Google Cloud Filestore is more expensive but offers better performance and reliability for large-scale, collaborative workloads.

4. How should a professional assess the security implications of using Google Cloud's Identity and Access Management (IAM) vs. traditional role-based access control (RBAC) in a multi-cloud environment?

   • Google Cloud IAM provides fine-grained access control with policy inheritance and predefined roles, making it easier to manage permissions across multiple cloud services, while traditional RBAC may require more manual configuration and maintenance in a multi-cloud setup.
   • Google Cloud IAM is more secure due to its integration with Google's security infrastructure, whereas traditional RBAC relies on the underlying cloud provider's security measures, which may vary across different cloud platforms.
   • Google Cloud IAM offers better auditing and logging capabilities, allowing for more detailed tracking of access and changes, while traditional RBAC may lack comprehensive auditing features in a multi-cloud environment.
   • Google Cloud IAM is more cost-effective for small-scale deployments, while traditional RBAC is better suited for large-scale, enterprise-level security management in a multi-cloud environment.

5. What strategy should be applied when designing a disaster recovery plan for a critical Google Cloud-based application to ensure high availability and data durability?

   • Implement a multi-region architecture using Google Cloud's regional and zonal resources, replicating data across multiple regions and utilizing Cloud Load Balancing to distribute traffic, ensuring failover capabilities and data redundancy.
   • Use Google Cloud's Backup and DR service to create regular backups of the application data and configure automated failover to a secondary region in case of a primary region outage.
   • Deploy the application in a single region with multiple zones, utilizing Google Cloud's zonal services for high availability within the region, and manually migrate the application to a different region in case of a disaster.
   • Rely on Google Cloud's built-in redundancy and failover mechanisms, assuming that the platform's inherent design will handle any potential disasters without additional configuration or planning.