InterviewStack.io LogoInterviewStack.io

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.

HardSystem Design
0 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.
EasyTechnical
0 practiced
Describe the differences between virtual machines (VMs), containers, and serverless platforms. As a Data Analyst deciding where to run analytics workloads (nightly ETL, ad-hoc SQL exploration, and short-lived model scoring), explain which platform you'd choose for each workload and why. Discuss startup time, scaling behavior, operational overhead, cost characteristics, and team learning curve.
MediumSystem Design
0 practiced
Outline an analytics architecture to support near-real-time dashboards (data freshness <=5 seconds) for 100k active users showing aggregated metrics and leaderboards. Include choices for ingestion, streaming processing, serving layer, storage, and caching. Explain trade-offs in latency, cost, operational complexity, and explainability for downstream analysts.
MediumBehavioral
0 practiced
Tell me about a time you advocated for adopting a managed service (e.g., managed warehouse or streaming service) despite resistance due to perceived higher costs. How did you build the business case, quantify trade-offs, and what was the final outcome?
HardTechnical
0 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.

Unlock Full Question Bank

Get access to hundreds of Technology and Platform Selection interview questions and detailed answers.

Sign in to Continue

Join thousands of developers preparing for their dream job.