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.

MediumTechnical

0 practiced

Explain how to design a Pub/Sub-based system that preserves per-customer message ordering while enabling horizontal scalability. Discuss ordering keys, throughput limits, sharding strategies, and downstream processing implications.

EasyTechnical

0 practiced

List and briefly describe primary methods to ingest data into BigQuery (both batch and streaming): Cloud Storage load jobs, Streaming Inserts, Dataflow, Datastream, and Cloud Data Fusion. For each, note typical throughput/latency characteristics and practical limitations.

MediumTechnical

0 practiced

Design a deduplication and idempotency strategy for an at-least-once streaming ingestion pipeline into BigQuery. Include message identifiers, Dataflow stateful dedupe patterns, BigQuery unique constraints/workarounds, and TTL strategies to bound state costs.

MediumTechnical

0 practiced

Describe how you would set up monitoring, alerting, and SLOs for an end-to-end streaming pipeline (Pub/Sub -> Dataflow -> BigQuery). Which metrics would you track, what dashboards/alerts would you create, and what runbook actions correspond to common alert types?

MediumTechnical

0 practiced

For a regulated client storing PII, outline a security design using GCP services covering: IAM controls for BigQuery and Cloud Storage, encryption with CMEK (Cloud KMS), VPC Service Controls, DLP scanning/tokenization, and audit logging. Explain trade-offs and operational tasks to maintain compliance.

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