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Technology Stack Knowledge Questions

Assess a candidate's practical and conceptual understanding of technology stacks, including major programming languages, application frameworks, databases, infrastructure, and supporting tools. Candidates should be able to explain common use cases and trade offs for languages such as Python, Java, Go, Rust, C plus plus, and JavaScript, including differences between compiled and interpreted languages, static and dynamic type systems, and performance characteristics. They should discuss application frameworks and libraries for frontend and backend development, common web stacks, service architectures such as monoliths and microservices, and application programming interfaces. Evaluate understanding of data storage options and trade offs between relational and non relational databases and the role of structured query language. Candidates should be familiar with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, infrastructure components including containerization and orchestration tools such as Docker and Kubernetes, and development workflows including version control, continuous integration and continuous delivery pipelines, testing frameworks, automation, and infrastructure as code. Assess operational concerns such as logging, monitoring and observability, deployment strategies, scalability, reliability, fault tolerance, security considerations, and common failure modes and mitigations. Interviewers may probe both awareness of specific tools and the candidate's depth of hands on experience, ability to justify technology choices by evaluating trade offs, constraints, and risk, and willingness and ability to learn and evaluate new technologies rather than claiming mastery of everything.

HardSystem Design
65 practiced
Design SLOs and SLIs for a critical daily ETL pipeline that feeds dashboards. Define measurable SLIs (freshness, completeness, latency, error rate), set realistic SLO targets and error budgets, propose alert thresholds and escalation paths, and outline runbook actions for common failure scenarios such as delayed runs, partial writes, and data-quality violations.
MediumTechnical
85 practiced
Describe a Terraform plan in pseudocode or resource list to create an AWS S3 bucket for raw data with server-side encryption enabled, versioning enabled, a lifecycle rule that transitions objects older than 90 days to Glacier, and a bucket policy that blocks public access while allowing access from a specific IAM role. Include notes on state locking and protecting secrets in your Terraform workflow.
EasyTechnical
104 practiced
Describe the main Infrastructure-as-Code (IaC) tools used for data platforms: Terraform, CloudFormation, and Pulumi. Focus on practical differences: language choices, state management, drift detection, testing frameworks, multi-cloud support, and team onboarding. Which would you recommend for a small data engineering team that needs repeatable AWS deployments and why?
HardTechnical
87 practiced
Plan a migration from an on-prem Hadoop ecosystem (HDFS, YARN, Hive, MapReduce) to a cloud-native stack (S3, EMR/Databricks, Presto/Trino). Define data transfer methods, metadata and permissions migration, validation and testing steps, initial snapshot and incremental sync strategy to minimize downtime, rollback contingencies, and key operational differences (cost models, monitoring, and security).
HardSystem Design
68 practiced
An Airflow deployment is experiencing contention where many DAGs overload worker queues causing priority starvation and missed SLAs. Propose a refactor plan that includes DAG-level changes, priority and pool settings, executor/backend choices (Celery vs KubernetesExecutor), autoscaling strategy, resource requests/limits, and a testing approach to validate behaviour before rollout.

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