Data Organization and Infrastructure Challenges Questions

Demonstrate knowledge of the technical and operational problems faced by large scale data and machine learning teams, including data infrastructure scaling, data quality and governance, model deployment and monitoring in production, MLOps practices, technical debt, standardization across teams, balancing experimentation with reliability, and responsible artificial intelligence considerations. Discuss relevant tooling, architectures, monitoring strategies, trade offs between innovation and stability, and examples of how to operationalize models and data products at scale.

HardTechnical

0 practiced

As a staff-level engineer, design a policy/framework to let data scientists run experiments safely while protecting production systems. The policy should cover sandboxing, quotas, data access permissions, metrics isolation, and rollout pathways for successful experiments to production. Include enforcement mechanisms and incentives for compliance.

MediumTechnical

0 practiced

Write pseudocode or a Python function showing how a streaming pipeline should apply event-time watermarking and drop or tag late events that arrive beyond a configurable lateness threshold (e.g., 10 minutes). Assume you are using Apache Beam or a similar model. Explain how watermark advancement affects windowed aggregations.

MediumTechnical

0 practiced

List common forms of technical debt specific to data infrastructure and ML pipelines (give at least 6). For each form, propose measurable signals that it exists and a prioritization framework to decide which debts to pay down first.

EasyTechnical

0 practiced

You need to produce a stratified sample of a very large dataset for quick model prototyping using PySpark. The dataset is too big to fit on the driver. Describe and show PySpark DataFrame code that performs a reproducible stratified sample by a categorical column `label` (imbalanced classes), preserving the relative class proportions and ensuring the sample size is approximately N rows.

MediumTechnical

0 practiced

Write Spark SQL (or PySpark DataFrame code) to compute a per-user 7-day rolling average of `purchase_amount` using window functions. The input table `purchases(user_id STRING, amount DOUBLE, event_date DATE)` may have multiple purchases per day. Show sample input and expected output for one user.

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