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

0 practiced

Explain how BigQuery supports nested and repeated fields (STRUCT and ARRAY). When is a nested schema preferable to normalizing data into multiple tables? Provide an example analytic query that benefits from nesting.

HardTechnical

0 practiced

A mission-critical streaming Dataflow job performing aggregations has latency spikes and high worker costs. Describe a structured troubleshooting and optimization approach: which metrics and logs you would inspect, key tuning parameters (autoscaling, worker types, fusion, streaming engine), and possible algorithmic changes to reduce cost and latency.

EasyTechnical

0 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

0 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?

MediumSystem Design

0 practiced

Outline an orchestration strategy using Cloud Composer (Airflow) to coordinate nightly batch ETL jobs that move raw data from Cloud Storage into partitioned BigQuery tables. Include DAG design (sensors/triggers), retries, idempotency, and backfill approaches.

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