InterviewCloud
GCP questions for BigQuery, Vertex AI, GKE, Pub/Sub, Dataflow, and production ML systems.
Register the model, deploy to an endpoint, configure scaling, monitor predictions, and control rollout.
Include Artifact Registry, model registry, online prediction endpoints, traffic splitting, Cloud Monitoring, model evaluation, and IAM boundaries.
Partition, cluster, reduce scanned columns, use materialized views, and monitor query plans.
Interviewers want you to understand columnar storage, bytes scanned, partition pruning, clustering selectivity, slots, and avoiding SELECT * on large tables.
Publish events to Pub/Sub, process them with Dataflow, handle windows, write to storage, and monitor lag.
A good answer covers event time vs processing time, retries, dead-letter topics, idempotent writes, watermarks, autoscaling, and schema evolution.
Use Cloud Run for simple serverless containers and GKE when you need Kubernetes control or complex orchestration.
Compare operational overhead, networking, autoscaling, workloads, GPU needs, service mesh, and platform team maturity.
Professional Machine Learning Engineer and Professional Data Engineer are most relevant for AI/data interviews.
Use certs to organize study around BigQuery, Vertex AI, pipelines, IAM, monitoring, and cost. Pair them with hands-on deployment projects.