Practice Compute Engine, Cloud Functions, and Google Cloud services with real scenarios
Start Practicing NowMaster Compute Engine, Cloud Storage, and GCP fundamentals
Learn Cloud Functions, Cloud Run, and serverless patterns
Understand BigQuery, Cloud SQL, and ML services
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Answer realistic Google Cloud interview questions in a simulated environment
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Compute Engine and App Engine
Cloud Functions and Cloud Run
Cloud Storage and databases
VPC and networking
IAM and security
BigQuery and data analytics
A: Compute Engine: VMs, full control, IaaS. App Engine: PaaS, fully managed, standard (containers) or flexible (any runtime). Cloud Run: serverless containers, Knative-based, auto-scale to zero. Use Compute for control, App Engine for web apps, Cloud Run for containerized services/APIs.
A: BigQuery is serverless data warehouse for analytics. Columnar storage, SQL queries, petabyte scale. Pay-per-query or flat-rate pricing. Use for: analytics, data warehousing, log analysis, ML training data. Not for: transactional workloads, real-time updates, sub-second queries. Supports streaming inserts, external tables.
A: IAM controls who (identity) has what access (role) to which resource. Hierarchy: organization → folder → project → resource. Roles: primitive (owner, editor, viewer), predefined (service-specific), custom. Policies bind members to roles. Service accounts for app authentication. Use principle of least privilege.
A: GCP has global private network with edge locations. Load balancing types: Global HTTP(S) (anycast IP, SSL termination), TCP/UDP (regional), Internal (within VPC). Benefits: low latency, DDoS protection, auto-scaling. Cloud CDN for content delivery. Supports backend services with health checks.
Master gcloud CLI for resource management and deployments
Understand GCP's unique services: BigQuery, Pub/Sub, Cloud Spanner
Know Kubernetes Engine (GKE) for container orchestration
Practice with Cloud Functions and Cloud Run for serverless
Understand VPC networking, firewall rules, and Cloud NAT
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