Intermediate everything in Data Science & Analytics, page 3
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.
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.
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.
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.
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.
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.
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.
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.
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 does a U-shaped EDA pattern influence feature engineering?
Add age squared or splines; trees handle splits but explicit terms aid linear models.
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 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.
When is streaming better than batch, and what are its infrastructure challenges?
Tests if you separate low-latency decisions from analytics and know ops pain. Good answer: fraud detection vs batch; cite exactly-once, state recovery, backpressure, schema drift. Red flag: calling streaming faster batch while ignoring backpressure or state.
How would you partition a massive user events table?
Tests whether you map query patterns to storage layout to cut bytes scanned. Strong answers pick time-based partitioning for range pruning, cluster by high-cardinality filters, and justify against WHERE clauses.
What is data pipeline idempotency and how do you design for it?
This tests resilient pipeline design under failure. A strong answer defines idempotency as identical output on repeated runs, highlights safe retries and partial failure recovery, and proposes idempotency keys with atomic writes for daily API loads.
Why is scaling unnecessary for trees but critical for SVM or K-Means?
Tests whether you understand model internals. Trees split on rank order, so scale is irrelevant. SVM and K-Means rely on distance or margin geometry, making magnitude dominate.
What is data leakage in preprocessing and cross-validation?
This tests recognition of data leakage through preprocessing statistics. A strong answer defines leakage, describes scaling using global statistics before CV splits, and states transformers must be fit per training fold.
Implement OAuth 2.0 flow to get an access token for API requests
Tests your grasp of OAuth 2.0 grant-type selection and token lifecycle. Strong answers match the script context to client credentials or authorization code flow, detail the token endpoint exchange, and address refresh and expiry.
SQL or NoSQL for high-volume semi-structured event ingestion?
Choose NoSQL for schema-less landing; use SQL downstream for structured analytics.
How would you scrape a page with dynamically loaded JavaScript content?
It tests if you know dynamic pages need a real renderer. A great answer names Playwright or Selenium, uses explicit waits for elements, and extracts via DOM or network interception. Red flag: suggesting only static parsers like BeautifulSoup or blind sleeps.
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