Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.
EasyTechnical
0 practiced
Compare S3 (or other object stores) with HDFS for big-data workloads. Discuss consistency semantics (atomicity and listing consistency), POSIX semantics (rename semantics), latency and throughput characteristics, operational concerns (namenode vs object-store), and how these differences affect Spark/Hadoop job semantics and pipeline patterns.
MediumTechnical
0 practiced
Athena queries on Parquet files in S3 are slow. Provide a set of concrete improvements you would make: file sizing and compaction strategy, partitioning scheme, choosing compression codec and level, enabling predicate pushdown and column pruning, and steps to compact small files. Explain trade-offs between compaction frequency and freshness.
HardSystem Design
0 practiced
Design an end-to-end lakehouse architecture (Delta Lake, Iceberg, or similar) for a 100+ TB dataset that supports batch and streaming writes, ACID transactions, time travel, schema evolution, data compaction, and serving both BI and ML workloads. Describe storage layout, metadata service, partitioning, compaction/optimize strategy, concurrency control, ingest guarantees, and access controls.
MediumTechnical
0 practiced
Explain how to implement idempotent ETL tasks and safe retries in Airflow for a multi-step pipeline that writes to a data warehouse. Provide Python pseudocode or SQL examples showing upsert patterns, use of staging tables, run identifiers, and how you persist checkpoints so retries do not duplicate data or corrupt transactional boundaries.
HardSystem Design
0 practiced
Design a feature store that supports both online low-latency lookups for serving and offline feature tables for batch training. Discuss schema design, feature versioning and lineage, materialization pipelines (streaming and batch), online-store choices (Redis/DynamoDB), consistency/freshness guarantees, TTL/eviction policies, and monitoring for feature drift.
Unlock Full Question Bank
Get access to hundreds of Technical Tools and Stack Proficiency interview questions and detailed answers.
