Intermediate interview questions in Data Science & Analytics, page 2
How do you detect and handle outliers in multivariate datasets?
This tests statistical judgment beyond univariate thresholds. A strong answer names multivariate methods like Mahalanobis distance or isolation forests, PCA or pair plots for visualization, and checks for measurement error before removal.
How do you visualize clusters in 100-dimensional numerical data?
Tests PCA versus t-SNE tradeoffs for high-dimensional visualization. Strong answers use PCA first for global variance and outliers, then t-SNE with perplexities 5-50 run to stability. Red flag: interpreting t-SNE cluster sizes or distances as literal metrics.
How does a U-shaped EDA pattern influence feature engineering?
Add age squared or splines; trees handle splits but explicit terms aid linear models.
Explain bias-variance tradeoff and how regularization or tree depth manage it
Tests understanding of generalization error decomposition. Define bias as underfitting and variance as sensitivity to training noise; show regularization and shallow trees trade excess variance for slightly higher bias. Red flag: claiming both can hit zero.
How would feature engineering for categoricals differ for logistic regression versus LightGBM?
It tests model-specific encoding decisions. Logistic regression needs one-hot to avoid false ordinality; tree models like LightGBM use ordinal encoding since splits rely on thresholds, not distance.
Random Forest versus Gradient Boosting
Random Forest trains deep trees in parallel and averages to cut variance; boosting builds shallow trees sequentially, each correcting prior errors to cut bias, often higher accuracy but…
What is cross-validation and why is it more robust than a holdout split?
A single split is noisy and wastes data; k-fold rotates each fold as test, averages scores, and trains on all data.
Why not stop an A/B test when it looks significant early?
Tests whether you understand repeated looks inflate false positives. The term is peeking: checking daily can turn a 5% Type I error rate into roughly 15% by day 3. Red flag: citing "low sample size" without stating that early stopping invalidates the p-value.
How do network effects violate A/B tests and how to mitigate them?
Tests SUTVA violations and network experiment design. Answers note treated users alter control outcomes, then propose social-graph cluster randomization to isolate spillovers. Red flag: ignoring peer-to-peer spillover and using user-level randomization.
Analyzing skewed revenue-per-user experiments
Heavy tails inflate variance and slow significance, and the mean is dominated by whales; mitigate via winsorization or capping, log transforms, CUPED variance reduction, or bootstrap and rank tests.
What is Simpson's Paradox and how can it bias A/B tests?
Tests whether you recognize that aggregate trends can reverse within subgroups. A strong answer defines the paradox, gives an A/B example where treatment wins overall but loses in every segment due to skewed allocation, and prescribes stratified analysis.
Describe Transformer architecture and why self-attention beats recurrence
This tests parallelization and long-range dependencies. A strong answer outlines the encoder-decoder stack with multi-head self-attention, contrasts O(1) sequential steps versus RNNs' O(n) unrolling, and warns that describing it as averaging misses key ideas.
How do you leverage and fine-tune BERT for niche classification?
Tests transfer learning with scarce labels. Outline: pick a domain-adjacent checkpoint, add a classification head, use learning rates near 2e-5 with early stopping, and stratify tiny validation splits.
RL components and how Q-learning works
Agent acts on the environment, observes state and reward, seeking to maximize cumulative discounted reward; Q-learning iteratively updates Q(s,a) toward reward plus discounted best…
Walk me through a CNN's layers for image classification
Tests hierarchical feature extraction in CNNs. Answer: conv filters learn edges-to-objects with shared weights, pooling reduces dimensions and adds invariance, fully-connected layers classify.
Spark broadcast join versus shuffle join
A broadcast join sends the small table to every executor so the large table joins locally with no shuffle of its rows; the default sort-merge join shuffles both tables across the network, which is costly.
Data skew in Spark and salting
Data skew is uneven key distribution sending most rows to one partition and straggler task; salting appends a random suffix to hot keys to spread them across partitions, joining in two…
Spark RDDs, DataFrames, and Datasets
RDDs are low-level typed object collections with no built-in optimization; DataFrames are named columns optimized by Catalyst and Tungsten; Datasets add compile-time type safety in…
Reconcile rising sign-ups with falling revenue per user
Reconcile the metrics via total revenue and segment mix, frame the tradeoff, recommend an action.
Two ways accurate data can still mislead in a chart
Name distortions like truncated axes or cherry-picked ranges, give the fix for each, explain why each misleads.
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