data quality
Applied AI interview questions tagged data quality, across every topic.
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Concepts behind "data quality"
The curriculum that explains the ideas these questions test.
Foundational
Data Quality and ContractsModels and analytics are only as good as their data, and a silent upstream data change (a renamed column, a units switch, a spike in nulls) corrupts everything downstream with no error. Data quality means automated checks (schema, ranges, nulls, freshness, volume, uniqueness) plus data contracts between producers and consumers enforced in CI. Applied-AI interviews probe it because 'garbage in, garbage out' is the most common, hardest-to-diagnose cause of model and dashboard failures.🗄️ Data & SQL Engineering
Core
Handling Missing and Corrupted DataMissing data has three mechanisms (MCAR, MAR, MNAR) and the mechanism decides whether dropping rows is safe or biased and which imputation is valid. Beyond filling values, missingness itself is often a feature, and naive imputation is a classic source of leakage. Applied AI interviews probe it because how you handle gaps quietly determines whether your model is biased before training even starts.📊 Evaluation & ML FoundationsSign in
Core
Label Noise and Weak SupervisionLabel noise is errors in your training labels, and it caps the accuracy a model can reach no matter how good the architecture is. Weak supervision is the practice of generating training labels programmatically (labeling functions, distant supervision) instead of by hand, trading some accuracy for scale. Applied AI interviews probe this because real datasets are noisy, the difference between a model stuck at 78 percent and one at 90 percent is often the labels and not the model, and candidates who understand confident learning and clean test sets are the ones who can actually move metrics.📊 Evaluation & ML FoundationsSign in
Core
Synthetic Data GenerationSynthetic data is training or eval data produced by a model, a simulator, or a program rather than collected from the real world, used to bootstrap labels, cover rare cases, and distill a larger model into a smaller one. Its value hinges on quality, diversity, and the absence of leakage between your generator and your eval. Applied AI interviews probe it because candidates reach for it as a free fix and miss the failure modes: distribution mismatch, eval contamination, and model collapse from training on a model's own outputs.📊 Evaluation & ML FoundationsSign in
Core
Model Debugging MethodologyModel debugging is the systematic process of root-causing why a model underperforms: deciding whether the cause is the data, the features, the labels, model capacity, or the evaluation itself, rather than blindly tuning hyperparameters. The method leans on error analysis over slices and the train/val/test gap ladder to localize the failure before fixing it. Applied AI interviews probe it because most candidates jump to bigger models or more tuning when the real bug is a leaky feature, a noisy label set, or a broken eval.🔁 MLOps & LifecycleSign in
Core
Deduplication (Exact and Fuzzy)Duplicates creep into data from retries, joins, and multiple sources, and they corrupt counts, training sets, and aggregates. Exact dedup is a window-function job: ROW_NUMBER over a key, keep rank 1. Fuzzy/near-duplicate dedup (same content, slightly different) needs similarity, embeddings or MinHash/LSH to find near-matches at scale without comparing all pairs. Applied-AI interviews probe it because deduping training data and pipeline outputs is constant, and naive all-pairs comparison does not scale.🗄️ Data & SQL EngineeringSign in