36 practice questions with answers for Data Science interviews in Delhi.
Data Science jobs in Delhi
Delhi NCR has one of India's biggest pools of IT, consulting, banking and start-up jobs. Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles.
Below are 36 questions that match the mix of service, product and domain employers hiring data scientists in Delhi.
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01
Write SQL to find customers who ordered in January 2026 but not in February 2026.
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'
);
02
Why do we use activation functions? Why is ReLU popular?
Power is the probability of detecting a real effect. Increase it with a larger sample, a bigger effect size, lower noise or a higher significance level.
04
Write OpenCV code to read an image, convert it to grayscale, resize it and detect edges.
A residual block adds its input to its output, so the layers learn a residual F(x) and the block outputs F(x) plus x. If extra layers are not useful, they can learn to output near zero and the block behaves like identity. The skip path also lets gradients flow directly to early layers, reducing vanishing gradients. This is why ResNets with 50 or 100-plus layers train well where plain deep networks degrade.
06
Tell me about a time you deployed a model under a tight deadline.
Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.
They build a list in one readable expression, for example [x*x for x in data if x > 0]. They are usually faster than an equivalent for-loop with append.
A normal subquery runs once on its own and its result is used by the outer query, for example WHERE price > (SELECT AVG(price) FROM products). A correlated subquery refers to columns of the outer query, so it is logically evaluated once per outer row, for example comparing each product with its own category average. Correlated subqueries can be slow; joins or window functions are usually faster alternatives.
10
Write Python code that validates prediction requests with Pydantic before passing them to a model.
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
It checks whether observed category counts differ from expected counts, used for independence between two categorical variables and for goodness of fit.
13
What is normalisation, and why are analytics tables often denormalised?
Normalisation splits data into related tables to remove redundancy, so each fact is stored once, which suits transactional systems with many updates. Analytics workloads mostly read large volumes, and joins across many tables are slow and error-prone. So warehouses often use denormalised, wide tables or star schemas that repeat some attributes. The trade-off is more storage and the need to keep copies consistent through pipelines.
14
Your sales forecast scores well in k-fold cross-validation but fails in production. What could be wrong with the validation?
Random k-fold mixes future and past rows, so the model trains on data from after the period it predicts. That leaks information and inflates scores. Use time-based splits instead: train on an expanding or rolling window and validate on the next period, like TimeSeriesSplit in scikit-learn. Leave a gap between train and validation when features use lagged values, so the test reflects real forecasting conditions.
15
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
Accuracy, F1 and confusion matrices for classification, BLEU or ROUGE for generation and summarisation, perplexity for language models, plus human review.
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.
19
Write Python code to compute the Population Stability Index for a feature to detect drift.
A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.
21
What is the difference between iterators and iterables in Python?
An iterable can produce an iterator (it has __iter__); an iterator has __next__ and remembers its position. A for-loop calls iter() on the iterable and then next() repeatedly.
22
Write a Python function to flatten a nested list of any depth.
def flatten(items):
out = []
for x in items:
if isinstance(x, list):
out.extend(flatten(x))
else:
out.append(x)
return out
print(flatten([1, [2, [3, 4]], 5])) # [1, 2, 3, 4, 5]
23
Write Python code to run an ADF stationarity test and difference the series if needed.
from statsmodels.tsa.stattools import adfuller
def is_stationary(s, alpha=0.05):
p_value = adfuller(s.dropna())[1]
return p_value < alpha
series = s.copy()
d = 0
while not is_stationary(series) and d < 2:
series = series.diff()
d += 1
print('differencing order d =', d)
A large language model is a neural network, usually a decoder-only transformer, trained on huge amounts of text to predict the next token. With billions of parameters and further instruction tuning, it can answer questions, summarise, write code and follow instructions. It does not look up facts; it generates likely text based on patterns learned in training, so it can be fluent but wrong. Data scientists use LLMs through APIs or open-weight models.
The receptive field is the region of the input image that can influence one activation. Early layers see small patches like edges, and deeper layers see larger regions, enabling recognition of whole objects. It grows with kernel size, depth, stride and pooling, and dilated convolutions increase it without extra parameters. If the receptive field is smaller than the objects you care about, the network cannot capture their full context.
ANOVA, analysis of variance, tests whether three or more group means are equal. It compares variation between groups to variation within groups using an F statistic. For example, comparing average order value across four city tiers. A significant result says at least one mean differs but not which one, so you follow up with post-hoc tests like Tukey's HSD. It assumes independent samples, roughly normal residuals and similar variances.
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.
It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.
30
Your forecast uses actual daily temperature as a feature and looks great in backtesting, but it performs worse in production. Why?
In backtesting you used the actual temperature for future days, which you will never know at forecast time. In production the model gets a weather forecast, which has its own error, so performance drops. This is a subtle form of leakage. Fix the backtest by using the weather forecast that was available on each forecast date, or lagged actuals, so offline results match what production can really achieve.
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.
An AI agent is an LLM that works in a loop: it plans a step, calls a tool, reads the result and decides the next step until the goal is met. For example, it might query a database, run Python on the result and write a summary. Agents handle multi-step tasks but are less predictable, slower and costlier than a single call. Good designs limit tools and steps, log every action and keep a human check for risky actions.
33
What is the difference between probability and likelihood?
Probability fixes the parameters and asks how likely different data are, for example the chance of 7 heads in 10 tosses if the coin is fair. Likelihood fixes the observed data and asks how plausible different parameter values are, for example which coin bias best explains 7 heads. The likelihood function is not a probability distribution over parameters. Maximum likelihood estimation picks the parameter value that maximises it.
34
What are lambda functions and where are they useful?
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
Bias occurs when the sample does not represent the population. Reduce it with random or stratified sampling, higher response rates and by checking sample demographics against the population.
Which companies hire Data Science professionals in Delhi?
Companies with large offices in Delhi include Samsung, Boston Consulting Group. Use their company pages below to practise employer-specific sets.
Are Data Science interviews in Delhi different from other cities?
The questions depend more on the company than the city. Delhi's mix of employers decides the style: Delhi itself hosts many corporate, government-tech and consulting offices, and most candidates also apply across Noida and Gurgaon, so interviews here mix service-company and product-company styles
How do I find Data Science jobs in Delhi?
Shortlist companies with Delhi offices, keep your CV focused on Data Science projects, apply on company career pages and job portals with the Delhi filter, and practise the questions on this page before each interview.