Technology Selection & Deep Technical Knowledge Questions
Deep understanding of specific technologies relevant to complex system design. Master databases (PostgreSQL, Cassandra, DynamoDB, Elasticsearch), message queues (Kafka, RabbitMQ), caching systems (Redis), search engines, and frameworks. Understand their strengths, weaknesses, trade-offs, operational characteristics, scaling patterns, and common pitfalls. Be able to justify technology choices based on specific system requirements.
HardSystem Design
0 practiced
Design an architecture for performing large-scale feature backfills and model retraining: include orchestrator (Airflow/Argo), compute (Spark/Beam), storage layout (versioned parquet/Delta), idempotency and checkpointing, and how to track and store feature versions to ensure experiment reproducibility. Address cost control and incremental vs full backfills.
EasyTechnical
0 practiced
As a data scientist working with production systems, explain the differences between OLTP and OLAP systems. Cover typical workload patterns (single-row reads/writes vs large analytic scans), latency and throughput expectations, storage formats (row vs columnar), indexing/partitioning implications, and name one concrete technology choice for each. Give a short example of when to route incoming application events to OLTP versus OLAP.
HardTechnical
0 practiced
For telemetry at 500k writes/sec, compare wide-column stores (Cassandra/Scylla), document stores (MongoDB), and key-value stores (DynamoDB) in terms of write scalability, compaction, consistency, ability to run aggregations, and operational overhead. Which would you recommend and why? Include mention of data modeling patterns to keep throughput high.
EasyTechnical
0 practiced
Describe Elasticsearch and its primary use-cases. Then explain a scenario where Elasticsearch would be a poor choice compared to an OLAP engine (e.g., Redshift/Snowflake) or a transactional DB (Postgres). Focus on data durability, complex aggregations, consistency, and write patterns.
MediumSystem Design
0 practiced
Design a feature store that supports both offline feature generation (for training) and online low-latency serving. Describe technology choices for offline store (Parquet on S3, Hive/Delta), online store (Redis, DynamoDB), feature versioning and lineage, freshness guarantees, and how you would keep them consistent.
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