Data Science Interview Questions in Gurgaon

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An epoch is one pass over the training data; a batch is the subset used for one update; an iteration is one weight update on a batch.

Write SQL to find customers who ordered in January 2026 but not in February 2026.

SQL for Data ScienceMedium

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SELECT DISTINCT o.customer_id FROM orders o WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01' AND NOT EXISTS ( SELECT 1 FROM orders f WHERE f.customer_id = o.customer_id AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01' );

What are tokens and the context window in an LLM?

Generative AI and LLMsEasy

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Tokens are the pieces of text an LLM reads and writes, often word parts; in English, one token is roughly three quarters of a word. The context window is the maximum number of tokens the model can consider at once, including your prompt, any documents and its answer. Costs and speed depend on token counts. Long documents beyond the window must be chunked, summarised or retrieved selectively.

What is a Poisson distribution used for?

Statistics and ProbabilityEasy

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Modelling the count of events in a fixed interval when events occur independently at a constant average rate, such as calls per hour or defects per batch.

What is the vanishing gradient problem?

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Gradients shrink as they pass through many layers (for example with sigmoid), so early layers barely learn. ReLU, residual connections and good initialisation help.

Tell me about a feature you engineered that significantly improved a model.

Feature EngineeringMedium

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Situation: our delivery time model kept underestimating late-night orders. Task: reduce large errors. Action: I noticed restaurant prep time depended on how busy the kitchen was, so I built a feature for active orders at that restaurant in the last 30 minutes, computed point in time. Result: MAE dropped by 14 percent and late-night errors fell the most. It taught me to study the worst errors before adding generic features.

Your neural network's training loss suddenly becomes NaN. What do you check?

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Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.

How do you handle missing values?

Python for Data ScienceEasy

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First understand why data is missing. Then drop rows or columns with too many nulls, impute with mean/median/mode or a model (KNN), add a 'was_missing' flag, and fit imputers on training data only.

What is named entity recognition?

Deep Learning and NLPEasy

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Named entity recognition finds and labels spans of text such as person names, organisations, locations, dates and amounts. For example, in an invoice email it can pull out the vendor, invoice number and total. It is usually framed as token classification with BIO tags. spaCy and fine-tuned transformer models are common tools. Domain-specific entities, like medicine names, usually need custom labelled training data.

What is a generator and when would you use one?

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A function using yield that produces values lazily, one at a time. It saves memory when processing large files or streams because the full sequence is never held in RAM.

What should you log for a production prediction service?

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For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.

Difference between a shallow copy and a deep copy?

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A shallow copy duplicates the outer object but shares nested objects. A deep copy (copy.deepcopy) recursively duplicates everything, so changes to nested items do not leak.

Write Python code to compute a 95% bootstrap confidence interval for the mean.

Statistics and ProbabilityMedium

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import numpy as np def bootstrap_ci(x, n_boot=10000, seed=42): rng = np.random.default_rng(seed) x = np.asarray(x) means = [rng.choice(x, size=len(x), replace=True).mean() for _ in range(n_boot)] return np.percentile(means, [2.5, 97.5]) print(bootstrap_ci([12, 15, 9, 20, 14, 11, 18]))

Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?

Time Series & ForecastingMedium

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The model is treating Diwali as noise because the festival moves on the calendar and a fixed yearly seasonality does not align. Add explicit holiday features: a flag for Diwali and a few days before and after, since buying starts early. Include other regional festivals and sale events too. Models like Prophet or gradient boosting with holiday features handle this well. Then backtest specifically on past festive periods to confirm the fix.

How does a multi-armed bandit differ from a classic A/B test?

A/B Testing & ExperimentationHard

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A classic A/B test splits traffic in fixed proportions until the end, then decides, which is best for learning and clean inference. A multi-armed bandit, such as Thompson sampling, shifts traffic towards better-performing variants as data arrives, reducing the cost of showing a weak variant. Bandits suit short-lived decisions like headlines or promotions. They make unbiased effect estimates and guardrail analysis harder, so they are poor for major product decisions.

Tell me about a time an experiment result changed a product decision.

A/B Testing & ExperimentationMedium

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Situation: the team was confident a redesigned home page would increase orders. Task: I owned the experiment design and analysis. Action: I set conversion as primary and session length and refunds as guardrails, ran it for two full weeks and found no lift in orders but a drop for older app versions. Result: we fixed the performance issue, retested, and shipped a version that raised orders by about 1.5 percent.

Your dataset is highly imbalanced (2% positives). How do you handle it?

Machine LearningMedium

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Use stratified splits, class weights, resampling (SMOTE or undersampling) and threshold tuning, and judge the model with PR-AUC or F1 instead of accuracy.

What is the difference between saving a model with pickle, joblib and ONNX?

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pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.

What are guardrails in LLM applications?

Generative AI and LLMsMedium

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Guardrails are checks around the model that keep inputs and outputs safe and on-topic. Input guardrails detect prompt injection, abuse or off-topic requests. Output guardrails check for personal data, toxic content, policy violations or invalid formats before the answer reaches the user. They can be rules, classifiers or a second LLM call. Good guardrails also define fallback responses and log blocked cases for review.

What is the difference between a star schema and a snowflake schema?

SQL for Data ScienceMedium

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A star schema has a central fact table, like sales with amounts and foreign keys, surrounded by denormalised dimension tables such as product, customer and date. A snowflake schema normalises dimensions further, for example product links to a separate category table. Star schemas need fewer joins and are faster and simpler for analysts. Snowflake schemas save storage and reduce duplication but make queries more complex.

Pandas: find customers whose monthly spend increased for three consecutive months.

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m = (df.assign(month=df['date'].dt.to_period('M')) .groupby(['customer_id', 'month'])['amount'].sum() .reset_index() .sort_values(['customer_id', 'month'])) m['up'] = (m.groupby('customer_id')['amount'].diff() > 0).astype(int) # three rises in a row = three consecutive increases m['streak'] = (m.groupby('customer_id')['up'] .rolling(3).sum().reset_index(level=0, drop=True)) result = m.loc[m['streak'] == 3, 'customer_id'].unique()

What is overfitting and how do you prevent it?

Machine LearningEasy

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The model memorises noise and fails on new data. Prevent it with more data, simpler models, regularisation, cross-validation, early stopping and dropout.

What are hybrid search and reranking in retrieval systems?

Generative AI and LLMsHard

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Hybrid search combines keyword search, like BM25, with vector search. Keywords catch exact matches such as product codes and names, while vectors catch meaning and paraphrases. Results are merged, often with reciprocal rank fusion. Reranking then takes the top 20 to 50 candidates and scores each one against the query with a cross-encoder model, which is slower but more accurate. Together they usually improve RAG answer quality noticeably.

You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?

Feature EngineeringMedium

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Target encoding replaces each category with the average target value for that category, for example the churn rate per city. It handles high-cardinality columns compactly. The main risk is leakage: if a row's own target is included in its encoding, the model sees the answer and overfits. Prevent it with out-of-fold encoding, smoothing toward the global mean for rare categories, and fitting only on training data.

How does a decision tree decide where to split?

Machine LearningMedium

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It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.

Your notebook runs on your laptop but fails on a colleague's machine. How do you make the work reproducible?

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First pin the environment: a requirements.txt or conda environment file with exact versions, or a Docker image. Set random seeds for NumPy, scikit-learn and any framework you use. Remove hard-coded local paths and read them from a config file. Make sure data comes from a shared, versioned location. Finally, restart the kernel and run the notebook top to bottom, so hidden cell order bugs are caught before sharing.

How do you detect and treat concept drift?

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Monitor input distributions and live metrics, compare to training baselines, and retrain or recalibrate on fresh data when performance drops.

What is chunking in RAG and how do you choose a chunk size?

Generative AI and LLMsMedium

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Chunking splits documents into smaller pieces before embedding them, so retrieval returns focused passages that fit in the prompt. Small chunks, like 200 tokens, are precise but lose context; large chunks, like 1,000 tokens, keep context but add noise and cost. Split on natural boundaries such as headings and paragraphs, add some overlap, and keep metadata like title and section. Then tune chunk size using retrieval metrics on real questions.

Tell me about a time you disagreed with a stakeholder about which metric to optimise.

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Situation: marketing wanted the lead model judged on overall accuracy. Task: agree on a metric that matched the goal. Action: I showed that accuracy rewarded predicting no conversion for everyone, then proposed precision in the top 1,000 leads per week, which matched the sales team's call capacity, with a simple chart. Result: they agreed, and the model with the better top-1,000 precision added 18 percent more conversions.

What is the difference between apply, map and applymap in Pandas?

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map works element-wise on a Series, apply works on a Series or along a DataFrame axis, and applymap (now DataFrame.map) applies a function to every cell.

Training a transformer on a GPU fails with an out-of-memory error. What can you do?

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Reduce the batch size and use gradient accumulation to keep the same effective batch. Shorten the maximum sequence length. Turn on mixed precision training with float16 or bfloat16. Use gradient checkpointing, which saves memory by recomputing activations. Freeze some layers or switch to a smaller model, or use parameter-efficient fine-tuning like LoRA. Also make sure no old tensors stay on the GPU between runs.

How does Kubernetes help in serving machine learning models?

MLOps and Model DeploymentHard

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Kubernetes runs containerised model services across a cluster. It restarts failed containers, scales the number of replicas based on traffic or CPU, rolls out new versions gradually and can roll back, and schedules GPU workloads on the right nodes. Tools built on it, like KServe or Seldon, add model-specific features such as canary releases and autoscaling to zero. The trade-off is operational complexity, so small teams often use managed services.

What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?

SQL for Data ScienceEasy

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COUNT(*) counts all rows, including rows with NULLs. COUNT(column) counts only rows where that column is not NULL. COUNT(DISTINCT column) counts unique non-NULL values. For example, in an orders table, COUNT(*) is the number of orders, COUNT(coupon_code) is orders that used a coupon, and COUNT(DISTINCT customer_id) is the number of unique buyers. Mixing them up is a common reporting bug.

Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?

A/B Testing & ExperimentationMedium

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Do not ship on conversion alone. Check whether the change shifted buyers to cheaper items, increased discount usage or pulled in low-value orders. Break down revenue into conversion times average order value, look at item mix, and confirm the revenue drop is statistically meaningful rather than noise. Then go back to the agreed primary metric and guardrails; if revenue is the business goal, it should guide the decision.

How do you choose a learning rate for a neural network?