data quality
AI, ML & GenAI 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 the data behind them, and a silent upstream data change (a renamed column, a units switch, a spike in nulls) corrupts everything downstream without raising an error. Data quality means automated checks (schema, ranges, nulls, freshness, volume, uniqueness) plus data contracts between producers and consumers enforced in CI. AI, ML, and GenAI interviews probe it because 'garbage in, garbage out' is the most common and 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. AI, ML, and GenAI engineer 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 means mistakes in your training labels, and it sets a hard ceiling on model accuracy regardless of how strong the architecture is. Weak supervision generates training labels through code (labeling functions, distant supervision) rather than by hand, giving up some accuracy in return for scale. AI, ML, and GenAI engineer interviews probe this because real datasets are messy, the gap between a model stuck at 78 percent and one hitting 90 percent usually comes down to labels rather than the model, and candidates who grasp confident learning and clean test sets are the ones who genuinely move metrics.📊 Evaluation & ML FoundationsSign in
Core
Synthetic Data GenerationSynthetic data is training or eval data made by a model, a simulator, or a program instead of gathered from the real world, used to bootstrap labels, cover rare cases, and distill a larger model down into a smaller one. Whether it helps depends on quality, diversity, and keeping leakage out between your generator and your eval. AI, ML, and GenAI engineer interviews probe it because candidates grab it as a free fix and overlook 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 work of root-causing why a model underperforms: judging 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 slice-level error analysis and the train/val/test gap ladder to pinpoint the failure before fixing it. AI, ML, and GenAI engineer interviews probe it because most candidates reach for 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 slip into data through 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 surface near-matches at scale without comparing all pairs. AI, ML, and GenAI interviews probe it because deduping training data and pipeline outputs is constant, and naive all-pairs comparison does not scale.🗄️ Data & SQL EngineeringSign in