Google Cloud Data Services Questions

Covers design and operational knowledge of Google Cloud Platform data products used for storage, processing, streaming, and analytics. Key skills include when and how to use BigQuery for serverless analytics and data warehousing, Dataflow for stream and batch pipelines built on Apache Beam, Cloud Storage for object store and data lake patterns, and Pub/Sub for messaging and event ingestion. Candidates should understand cost models, performance trade offs, schema and partitioning strategies, data ingestion and export patterns, pipeline monitoring and error handling, and integration between these services for end to end data solutions.

EasyTechnical

67 practiced

Describe Cloud Storage and common storage-class and lifecycle patterns you would propose for a data lake implementation on GCP. Provide examples for hot, warm, and cold data, explain how to implement lifecycle rules, and note cost vs retrieval-latency trade-offs.

EasyTechnical

70 practiced

Summarize BigQuery's pricing model: storage costs, on-demand (pay-per-query) vs flat-rate (slots), streaming insert pricing, and when to consider reservations. What simple levers would you use to control query costs for a small-to-medium client?

MediumTechnical

82 practiced

Design an error-handling and dead-letter strategy for a streaming pipeline built with Pub/Sub and Dataflow so that 'poisoned' messages don't block processing. Include DLQ design, retry/backoff policies, storage choices for DLQ (Pub/Sub, BigQuery, Cloud Storage), and operational alerting/triage steps.

HardSystem Design

80 practiced

Describe a robust approach to build end-to-end data lineage across Pub/Sub topics, Dataflow transforms, Cloud Storage objects, and BigQuery tables so auditors can trace a value from ingestion to report. Include how to instrument pipelines, where to store lineage metadata, and how consumers can query lineage.

EasyTechnical

75 practiced

Describe how Cloud Dataflow autoscaling behaves for both batch and streaming jobs. Which configuration knobs (worker type, maxWorkers, disk) can a Solutions Architect use to limit costs or control throughput, and what are the trade-offs?

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