Exam Code: GCP-DE
Exam Name: Data Engineer
Passing Score: 70%
Duration: 120 Minute
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60 Questions
60 Questions
60 Questions
60 Questions
Data Engineer
Exam Guide
Section 1: Designing and planning a cloud solution architecture (~24% of the exam)
1.1 Designing a solution infrastructure that meets business requirements. Considerations include:
● Business use cases and product strategy
● Cost optimization
● Supporting the application design
● Integration with external systems
● Movement of data
● Design decision trade-offs
● Build, buy, modify, or deprecate
● Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)
● Compliance and observability
1.2 Designing a solution infrastructure that meets technical requirements. Considerations include:
● High availability and failover design
● Elasticity of cloud resources with respect to quotas and limits
● Scalability to meet growth requirements
● Performance and latency
1.3 Designing network, storage, and compute resources. Considerations include:
● Integration with on-premises/multicloud environments
● Cloud-native networking (VPC, peering, firewalls, container networking)
● Choosing data processing technologies
● Choosing appropriate storage types (e.g., object, file, databases)
● Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)
● Mapping compute needs to platform products
1.4 Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:
● Integrating solutions with existing systems
● Migrating systems and data to support the solution
● Software license mapping
● Network planning
● Testing and proofs of concept
● Dependency management planning
1.5 Envisioning future solution improvements. Considerations include:
● Cloud and technology improvements
● Evolution of business needs
● Evangelism and advocacy
Section 2: Managing and provisioning a solution infrastructure (~15% of the exam)
2.1 Configuring network topologies. Considerations include:
● Extending to on-premises environments (hybrid networking)
● Extending to a multicloud environment that may include Google Cloud to Google Cloud communication
● Security protection (e.g. intrusion protection, access control, firewalls)
2.2 Configuring individual storage systems. Considerations include:
● Data storage allocation
● Data processing/compute provisioning
● Security and access management
● Network configuration for data transfer and latency
● Data retention and data life cycle management
● Data growth planning
2.3 Configuring compute systems. Considerations include:
● Compute resource provisioning
● Compute volatility configuration (preemptible vs. standard)
● Network configuration for compute resources (Google Compute Engine, Google Kubernetes Engine, serverless networking)
● Infrastructure orchestration, resource configuration, and patch management
● Container orchestration
Section 3: Designing for security and compliance (~18% of the exam)
3.1 Designing for security. Considerations include:
● Identity and access management (IAM)
● Resource hierarchy (organizations, folders, projects)
● Data security (key management, encryption, secret management)
● Separation of duties (SoD)
● Security controls (e.g., auditing, VPC Service Controls, context aware access, organization policy)
● Managing customer-managed encryption keys with Cloud Key Management Service
● Remote access
3.2 Designing for compliance. Considerations include:
● Legislation (e.g., health record privacy, children’s privacy, data privacy, and ownership)
● Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])
● Industry certifications (e.g., SOC 2)
● Audits (including logs)
Section 4: Analyzing and optimizing technical and business processes (~18% of the exam)
4.1 Analyzing and defining technical processes. Considerations include:
● Software development life cycle (SDLC)
● Continuous integration / continuous deployment
● Troubleshooting / root cause analysis best practices
● Testing and validation of software and infrastructure
● Service catalog and provisioning
● Business continuity and disaster recovery
4.2 Analyzing and defining business processes. Considerations include:
● Stakeholder management (e.g. influencing and facilitation)
● Change management
● Team assessment / skills readiness
● Decision-making processes
● Customer success management
● Cost optimization / resource optimization (capex / opex)
4.3 Developing procedures to ensure reliability of solutions in production (e.g., chaos engineering, penetration testing)
Section 5: Managing implementation (~11% of the exam)
5.1 Advising development/operation teams to ensure successful deployment of the solution. Considerations include:
● Application development
● API best practices
● Testing frameworks (load/unit/integration)
● Data and system migration and management tooling
5.2 Interacting with Google Cloud programmatically. Considerations include:
● Google Cloud Shell
● Google Cloud SDK (gcloud, gsutil and bq)
● Cloud Emulators (e.g. Cloud Bigtable, Datastore, Spanner, Pub/Sub, Firestore)
Section 6: Ensuring solution and operations reliability (~14% of the exam)
6.1 Monitoring/logging/profiling/alerting solution
6.2 Deployment and release management
6.3 Assisting with the support of deployed solutions
6.4 Evaluating quality control measures
Data Engineer
Exam Guide
Section 1: Designing and planning a cloud solution architecture (~24% of the exam)
1.1 Designing a solution infrastructure that meets business requirements. Considerations include:
● Business use cases and product strategy
● Cost optimization
● Supporting the application design
● Integration with external systems
● Movement of data
● Design decision trade-offs
● Build, buy, modify, or deprecate
● Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)
● Compliance and observability
1.2 Designing a solution infrastructure that meets technical requirements. Considerations include:
● High availability and failover design
● Elasticity of cloud resources with respect to quotas and limits
● Scalability to meet growth requirements
● Performance and latency
1.3 Designing network, storage, and compute resources. Considerations include:
● Integration with on-premises/multicloud environments
● Cloud-native networking (VPC, peering, firewalls, container networking)
● Choosing data processing technologies
● Choosing appropriate storage types (e.g., object, file, databases)
● Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)
● Mapping compute needs to platform products
1.4 Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:
● Integrating solutions with existing systems
● Migrating systems and data to support the solution
● Software license mapping
● Network planning
● Testing and proofs of concept
● Dependency management planning
1.5 Envisioning future solution improvements. Considerations include:
● Cloud and technology improvements
● Evolution of business needs
● Evangelism and advocacy
Section 2: Managing and provisioning a solution infrastructure (~15% of the exam)
2.1 Configuring network topologies. Considerations include:
● Extending to on-premises environments (hybrid networking)
● Extending to a multicloud environment that may include Google Cloud to Google Cloud communication
● Security protection (e.g. intrusion protection, access control, firewalls)
2.2 Configuring individual storage systems. Considerations include:
● Data storage allocation
● Data processing/compute provisioning
● Security and access management
● Network configuration for data transfer and latency
● Data retention and data life cycle management
● Data growth planning
2.3 Configuring compute systems. Considerations include:
● Compute resource provisioning
● Compute volatility configuration (preemptible vs. standard)
● Network configuration for compute resources (Google Compute Engine, Google Kubernetes Engine, serverless networking)
● Infrastructure orchestration, resource configuration, and patch management
● Container orchestration
Section 3: Designing for security and compliance (~18% of the exam)
3.1 Designing for security. Considerations include:
● Identity and access management (IAM)
● Resource hierarchy (organizations, folders, projects)
● Data security (key management, encryption, secret management)
● Separation of duties (SoD)
● Security controls (e.g., auditing, VPC Service Controls, context aware access, organization policy)
● Managing customer-managed encryption keys with Cloud Key Management Service
● Remote access
3.2 Designing for compliance. Considerations include:
● Legislation (e.g., health record privacy, children’s privacy, data privacy, and ownership)
● Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])
● Industry certifications (e.g., SOC 2)
● Audits (including logs)
Section 4: Analyzing and optimizing technical and business processes (~18% of the exam)
4.1 Analyzing and defining technical processes. Considerations include:
● Software development life cycle (SDLC)
● Continuous integration / continuous deployment
● Troubleshooting / root cause analysis best practices
● Testing and validation of software and infrastructure
● Service catalog and provisioning
● Business continuity and disaster recovery
4.2 Analyzing and defining business processes. Considerations include:
● Stakeholder management (e.g. influencing and facilitation)
● Change management
● Team assessment / skills readiness
● Decision-making processes
● Customer success management
● Cost optimization / resource optimization (capex / opex)
4.3 Developing procedures to ensure reliability of solutions in production (e.g., chaos engineering, penetration testing)
Section 5: Managing implementation (~11% of the exam)
5.1 Advising development/operation teams to ensure successful deployment of the solution. Considerations include:
● Application development
● API best practices
● Testing frameworks (load/unit/integration)
● Data and system migration and management tooling
5.2 Interacting with Google Cloud programmatically. Considerations include:
● Google Cloud Shell
● Google Cloud SDK (gcloud, gsutil and bq)
● Cloud Emulators (e.g. Cloud Bigtable, Datastore, Spanner, Pub/Sub, Firestore)
Section 6: Ensuring solution and operations reliability (~14% of the exam)
6.1 Monitoring/logging/profiling/alerting solution
6.2 Deployment and release management
6.3 Assisting with the support of deployed solutions
6.4 Evaluating quality control measures
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23 reviews for Data Engineer
Rakesh Sharma
January 18, 2025 at 05:26 am
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January 15, 2025 at 01:32 pm
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December 24, 2024 at 12:54 pm
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December 14, 2024 at 01:39 pm
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December 07, 2024 at 01:18 pm
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