Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Airtel hiring for Data Science roles
Bharti Airtel is an Indian telecom company providing mobile, broadband, DTH, enterprise connectivity and digital services such as Airtel Thanks and Airtel Payments Bank. Its digital and analytics teams work on network data, customer analytics and app development. It hires for software engineers, testers, network engineers and data professionals.
For Data Science roles, this Airtel practice set focuses most on Machine Learning (7), Python for Data Science (6), Computer Vision (6), Generative AI and LLMs (5). It has 16 easy, 24 medium questions, so you can start easy and work up to the harder ones.
Company type
Telecom
Headquarters
New Delhi, India
Main India offices
Gurugram, Bengaluru, Mumbai, Pune
Airtel Data Science interview process
Online assessment (aptitude + technical)
Technical interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Your SQL result does not match the number on the official dashboard. How do you resolve it?
I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.
02
Write Pandas code to cap outliers at the 1st and 99th percentile.
# fit limits on training data only, then apply to test
low, high = train['income'].quantile([0.01, 0.99])
train['income_capped'] = train['income'].clip(low, high)
test['income_capped'] = test['income'].clip(low, high)
A symmetric bell-shaped distribution defined by mean and standard deviation, where about 68%, 95% and 99.7% of values lie within 1, 2 and 3 standard deviations.
Differencing replaces each value with the change from the previous value: y_t minus y_(t-1). It removes a linear trend and often makes a series stationary. Seasonal differencing subtracts the value one season back, such as y_t minus y_(t-7) for daily data with a weekly cycle. Avoid over-differencing, because it adds noise and negative autocorrelation. In ARIMA, the d parameter is the number of times you difference.
A CTE, common table expression, is a named temporary result defined with WITH and used in the query that follows. It breaks complex SQL into readable steps and can be referenced more than once. A recursive CTE refers to itself: an anchor query starts the result and a recursive part keeps adding rows until none are added. It is used for hierarchies like employee-manager chains or generating date series.
06
What is the difference between a function and a method?
Hallucination is when a model produces confident text that is false or not supported by any source. It happens because the model generates plausible tokens rather than checking facts. Reduce it by grounding answers in retrieved documents, asking the model to cite sources and say I do not know when context is missing, using low temperature, and validating outputs with rules or a second checking step. Measure it with a labelled evaluation set.
Regularization adds a penalty on weights to reduce overfitting. L1 (Lasso) adds |w| and can push features to exactly zero. L2 (Ridge) adds w² and shrinks weights smoothly.
Calibration checks whether predicted probabilities match real frequencies (e.g. 70% predictions are right about 70% of the time). Fix with Platt scaling or isotonic regression.
A detector usually predicts many overlapping boxes for the same object. Non-maximum suppression keeps the box with the highest confidence, removes other boxes of the same class whose IoU with it exceeds a threshold such as 0.5, and repeats with the remaining boxes. A threshold that is too low can delete genuinely nearby objects in crowded scenes, so variants like soft-NMS lower scores instead of removing boxes.
12
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
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.
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.
15
Write OpenCV code to read an image, convert it to grayscale, resize it and detect edges.
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.
Bootstrapping estimates the uncertainty of a statistic by resampling your data with replacement many times, computing the statistic on each resample, and looking at the spread of results. It gives confidence intervals for things with no simple formula, like the median, a ratio or a model metric such as AUC. It needs no normality assumption, but it relies on the sample being representative and can be slow on very large data.
18
Write a Python function to compute IoU between two bounding boxes given as (x1, y1, x2, y2).
Set temperature to zero or near zero and fix other sampling settings, and use a seed where the API supports it, though outputs may still vary slightly. Pin the exact model version. Use a structured output format with fixed fields, and reduce ambiguity in the prompt. Cache and store each generated output with its inputs and version, so the stored result, not a regeneration, is what auditors see.
20
What is the difference between a set and a dictionary?
SVM finds the maximum-margin separating hyperplane. Kernels such as RBF compute similarity in a higher-dimensional space implicitly, so non-linear boundaries are possible.
25
What is the difference between a population and a sample?
A population is the full group you want to learn about, such as all customers of an app. A sample is the subset you actually observe, such as 5,000 surveyed customers. Numbers from the population are parameters, and numbers from a sample are statistics that estimate them. Good sampling, ideally random, lets you generalise from the sample to the population and measure the uncertainty using standard errors and confidence intervals.
26
What are polynomial features and what is the risk of using them?
Polynomial features add powers and products of existing features, like x squared and x times y, so a linear model can fit curves and interactions. The risk is that the number of features grows very fast with degree and input count, causing overfitting, slow training and multicollinearity. Use a low degree, scale features first, and add regularisation such as Ridge or Lasso to keep coefficients under control.
27
What is the difference between ROW_NUMBER, RANK and DENSE_RANK?
All three number rows within a window ordered by some column. ROW_NUMBER gives unique numbers even for ties: 1, 2, 3. RANK gives tied rows the same rank and skips the next numbers: 1, 1, 3. DENSE_RANK gives ties the same rank without gaps: 1, 1, 2. Use ROW_NUMBER to pick exactly one row per group, and DENSE_RANK for questions like the second highest distinct salary.
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.
29
A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?
The category dtype stores each unique value once and keeps small integer codes for every row. For a column like city with 50 unique values in 10 million rows, this can cut memory by more than 90 percent and speed up groupby and sorting. It helps when a column has low cardinality. It does not help for unique IDs or free text, and adding new categories later needs extra handling.
30
Your fraud model has 99 percent accuracy, but the business says it catches almost no fraud. Why?
If only 1 percent of transactions are fraud, a model that predicts not fraud for everything is 99 percent accurate and useless. Accuracy hides the minority class. I would check the confusion matrix, recall on fraud cases and precision-recall AUC. Then fix it with class weights, resampling or a lower decision threshold, and choose the threshold based on the cost of missed fraud versus false alerts.
Situation: in my final-year project, a teammate's cleaning script was one 600-line notebook that took 20 minutes to run. Task: I needed to reuse it for new data. Action: I split it into small functions, added docstrings, replaced row loops with vectorised Pandas code and wrote a few checks for row counts. Result: run time fell to 2 minutes and the team could reuse the functions. I learned to refactor in small, tested steps.
Data augmentation creates new training examples by applying realistic changes to existing ones. For images this means flips, rotations, crops and brightness changes. For text it can mean synonym replacement, back-translation or random word dropping. For audio, adding noise or shifting pitch. It improves generalisation when data is limited. Changes must keep the label correct; flipping a digit 6 can turn it into a 9.
35
Your logging system was down for four days, leaving gaps in a daily series. How do you handle them before modelling?
First make the gaps explicit by reindexing to a full daily calendar so missing days appear as NaN instead of silently disappearing. For short gaps, linear or time-based interpolation is usually fine. If there is strong weekly seasonality, fill using the same weekday from neighbouring weeks or a seasonal decomposition. Add a flag column marking imputed days, and never treat missing as zero unless zero really happened.
36
What is the difference between semantic, instance and panoptic segmentation?
Semantic segmentation gives each pixel a class, such as road or car, but does not separate two cars. Instance segmentation separates individual objects of countable classes, giving each car its own mask. Panoptic segmentation combines both: every pixel gets a class, and countable objects also get an instance ID, while background stuff like sky stays semantic. Autonomous driving and robotics often need panoptic or instance output.
The ROC curve plots true positive rate against false positive rate across thresholds. AUC is the probability that a random positive is ranked above a random negative; 0.5 is chance.
38
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
39
Why do residual connections make very deep networks easier to train?
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.
40
What is the difference between a base model and an instruction-tuned model?
A base model is trained only to predict the next token on large text, so it continues text rather than following requests. An instruction-tuned model is further trained on instruction and answer pairs, and often preference data, so it follows prompts, answers questions and behaves like an assistant. For apps you usually use instruction-tuned or chat models. Base models are mainly starting points for custom fine-tuning.
Tips to crack the Airtel Data Science interview
Learn telecom basics like ARPU, churn, 4G and 5G networks, and broadband rollout for case discussions.
Prepare analytics cases on reducing customer churn or improving network quality using data.
Read about Airtel's digital products like Airtel Thanks and Xstream, and its enterprise business Airtel Business.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Airtel interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Airtel Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical); Technical interview; Managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Airtel Data Science interview?
This practice set leans on Machine Learning (7), Python for Data Science (6), Computer Vision (6), Generative AI and LLMs (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Airtel interviewers ask you to walk through real work.
Are these the exact questions asked at Airtel?
No. These are curated practice questions that match the skills and difficulty usually tested in telecom interviews like Airtel's. They are not a leaked or verified list of past questions.
Where does Airtel hire Data Science professionals in India?
Airtel has large teams in Gurugram, Bengaluru, Mumbai, Pune. Openings and locations change often, so check the Airtel careers page and filter by city and experience.