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Technology and Platform Selection Questions

Evaluation and justification of technologies services and platforms used to implement systems across the stack. Candidates should be able to select compute options including virtual machines containers and serverless platforms as well as orchestration and workflow engines messaging systems batch and streaming processing engines object and block storage data warehouses and other data platforms. The topic encompasses comparing managed services and self managed deployments cloud versus on premise hosting and choosing frameworks runtimes and overall stacks based on workload characteristics. Assessment focuses on weighing trade offs across cost operational overhead reliability latency and throughput scaling characteristics vendor lock in development velocity team familiarity and learning curve maturity and community support security and compliance and monitoring and debugging complexity. Candidates should demonstrate how system requirements map to service capabilities justify build versus buy decisions and managed service choices design proof of concept experiments and outline migration and rollout planning while making pragmatic choices that balance performance cost and operational risk.

MediumTechnical
66 practiced
A third-party SaaS analytics provider offers an API and dashboarding capability vs. building the same dashboard in-house. As the Data Analyst, create a checklist of business and operational criteria you'd use to decide buy vs build for a critical KPI dashboard. Include speed to market, customization, data security, costs, and long-term maintenance.
HardSystem Design
58 practiced
As a senior data analyst, design a multi-tenant analytics platform for a SaaS product that requires strict resource isolation, per-tenant cost attribution, and the ability to run ad-hoc queries without noisy neighbor interference. Discuss tenancy models (shared schema, separate schema, isolated clusters), compute and storage choices, enforcement mechanisms, and monitoring for per-tenant quotas.
HardTechnical
63 practiced
Compare managed streaming plus serverless processing (e.g., managed Kafka alternatives + serverless Flink or cloud streaming + cloud functions) versus self-managed Kafka + Spark/Flink cluster for a low-latency (<200ms) event enrichment pipeline. Focus on throughput, tail latency, exactly-once semantics, operational complexity, and long-term cost at scale.
EasyTechnical
99 practiced
When selecting a BI tool (Tableau, Power BI, Looker), what criteria do you prioritize as a Data Analyst? Discuss data connectivity, governance and access controls, self-service capabilities, learning curve, collaboration features, licensing, and integration with existing data platforms.
MediumTechnical
50 practiced
Explain how partitioning, clustering, and indexing in a modern cloud data warehouse affect query performance and cost. Provide practical guidelines for selecting partition keys and clustering columns for time-series business metrics and for reducing bytes scanned.

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