Interview questions in Data Science & Analytics, page 7
Primary metric up, guardrail down: ship or not?
Tie metrics to business value, weigh short-term lift against retention damage, use guardrails and an overall evaluation criterion.
Catalyst Optimizer and Project Tungsten in Spark
Catalyst transforms logical plans with rules, picks physical plans by cost; Tungsten optimizes execution with off-heap memory and codegen.
Explain false positives and negatives for churn
False positive flags a loyal customer (wasted incentive), false negative misses a leaver (lost customer), tie to threshold choice.
Design an automated A/B test reporting system
Standardized metric definitions, automated stats with confidence intervals and guardrails, segment breakdowns, a clear ship recommendation.
Explain a loan denial with LIME or SHAP
LIME fits a local surrogate, SHAP attributes the prediction across features via Shapley values, both give per-feature contributions.
repartition() versus coalesce() in Spark
Repartition does a full shuffle and can increase or balance partitions; coalesce avoids a full shuffle and only reduces them.
Purpose of watermarks in Spark Structured Streaming
A watermark sets a threshold on event-time lateness, lets late data update windows up to that bound, and tells Spark when to finalize and drop old state.
Communicate a forecast interval to an executive
Give the point estimate but frame the range as scenarios, use a fan chart, tie the interval to planning decisions and risk.
A/B test two fraud models in production
Randomize by entity, consider shadow mode first, collect precision/recall and business loss, decide with significance and guardrails.
Audit an ML pipeline for GDPR compliance
Inventory data and check minimization, verify processing matches stated purpose, build lineage to trace any prediction's inputs.
Parquet versus CSV for analytical data lakes
Parquet stores by column enabling projection pushdown, compression, and predicate skipping; CSV is row-based, untyped, and slow to scan.
Interactive versus static plots for EDA
Interactive libraries win for exploring dense, high-cardinality, or multi-dimensional data via zoom, hover, and filtering; static plots win for reproducible, publication output.
Present a small but significant A/B test lift
Hypothesis, design and validity checks, result with effect size and interval, business impact of 0.5%, then a clear recommendation.
Explain an interaction effect to a non-statistician
Define interaction as it depends on, show separate slope lines per age group, give the business takeaway on targeting.
Demographic Parity versus Equalized Odds in hiring
Demographic parity equalizes selection rates regardless of qualification; equalized odds equalizes true and false positive rates across groups, conditioning on the true label.
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