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.

HardTechnical

0 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).

EasyTechnical

0 practiced

Define 'idempotency' and 'exactly-once' semantics in the context of batch and streaming data pipelines. Provide concrete examples of idempotent and non-idempotent sinks, and list practical techniques (dedup keys, idempotent writers, transactional batches) to implement idempotent behavior when writing to warehouses or object stores.

HardTechnical

0 practiced

Design an idempotent write strategy for batching streaming events into Redshift where network failures may cause producer retries and duplicates. Describe SQL-level and application-level techniques such as staging tables with MERGE statements, deduplication keys, watermarking logic, transactional batching, and how to integrate this strategy into a Kafka Connect or custom sink implementation.

EasyTechnical

0 practiced

For four common data engineering tasks—batch ETL, long-running Spark transformations, low-latency connectors, and a high-throughput streaming sink—recommend languages and frameworks from Python, Java/Scala, Go, Rust, and C++. Justify your choices in terms of ecosystem, performance, operator experience, and maintainability, and indicate when polyglot stacks make sense.

MediumSystem Design

0 practiced

Design a CDC (Change Data Capture) pipeline to capture row-level changes from a MySQL OLTP source and stream them into an analytics store such as Redshift or Snowflake. Include components (Debezium, Kafka, Kafka Connect), serialization format, schema evolution approach, initial snapshot/backfill strategy, ordering guarantees, handling deletes/tombstones, and how to reprocess historical data safely.

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