Data Processing & Matrix Operations Questions
Covers data processing concepts (ETL/ELT, batch and streaming pipelines, data transformation, quality, and schema design) as well as matrix operations (linear algebra basics such as matrix multiplication, decompositions, eigenvalues, and singular value decomposition) that underpin analytics workloads and ML systems within data engineering & analytics infrastructure.
EasyTechnical
45 practiced
Explain NumPy broadcasting rules and give two concrete examples where broadcasting can both simplify code and accidentally lead to subtle bugs in matrix operations used in feature pipelines or model code.
MediumSystem Design
45 practiced
Design a data validation and governance layer for an ETL pipeline that prepares ML training data. Requirements: detect schema changes, enforce data-quality rules, produce lineage metadata, and alert or roll back on violations. Describe components, how rules are authored, and how to integrate into CI/CD.
MediumTechnical
48 practiced
Compare sparse matrix storage formats CSR, CSC, and COO. For each format, say which workload it's best suited for: 1) repeated row-wise multiplication, 2) many incremental insertions of nonzeros, 3) efficient transpositions or column operations. Explain trade-offs for random access, memory, and construction cost.
HardTechnical
43 practiced
Discuss numerical issues when computing SVD for matrices with huge dynamic range or nearly repeated singular values. Compare algorithms like divide-and-conquer SVD and Jacobi SVD in terms of accuracy and speed. Explain implications of algorithm choice for downstream ML pipelines.
HardBehavioral
46 practiced
Behavioral / leadership: Describe a time you led the design and rollout of a major data preprocessing or matrix-computation infrastructure change (for example, introducing a feature store or migrating to distributed GPUs). Explain how you evaluated trade-offs, obtained stakeholder buy-in, measured success, and handled resistance or unexpected issues. Use the STAR method.
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