01
Explain p-value in simple words.
Show answer
The probability of seeing a result at least this extreme if the null hypothesis were true. A small p-value (for example < 0.05) is evidence against the null hypothesis.
Birlasoft Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Birlasoft hiring for Data Science roles
Birlasoft is an Indian IT services company and part of the CK Birla Group. It focuses on enterprise solutions such as Oracle, SAP and Microsoft applications, along with cloud, data and testing services for manufacturing, life sciences and energy clients. In India it hires for software engineers, ERP consultants, test engineers and data analysts.
For Data Science roles, this Birlasoft practice set focuses most on Computer Vision (8), Statistics and Probability (6), Deep Learning and NLP (4), SQL for Data Science (4). It has 16 easy, 23 medium, 1 hard questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Pune, India
- Main India offices
- Pune, Noida, Bengaluru, Chennai
Birlasoft Data Science interview process
- Online assessment (aptitude + reasoning)
- Technical MCQs
- Technical interview
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What is a large language model?
Show answer
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.
03
How does a CNN work?
Show answer
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
04
The average delivery time is 30 minutes, yet many customers complain about late orders. How do you investigate?
Show answer
An average hides the shape of the distribution. Delivery times are usually right-skewed, so a 30-minute mean can include many orders taking 60 minutes or more. Look at the median, the 90th and 95th percentiles and a histogram, then break them down by city, time of day and restaurant. Report the share of orders beyond the promised time, because that is what customers actually experience.
05
After joining orders with a promotions table, total revenue doubled. What went wrong?
Show answer
The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.
06
What is correlation and why does it not imply causation?
Show answer
Correlation measures linear association between variables (-1 to 1). A third factor, reverse causality or chance can create it, so causation needs controlled experiments.
07
What are RNNs and why did LSTMs improve them?
Show answer
RNNs process sequences with a hidden state but struggle with long dependencies. LSTMs add gates that control what to remember or forget, easing the vanishing gradient.
08
Tell me about the most challenging machine learning project you have worked on.
Show answer
Situation: I built a lead scoring model where only 3 percent of leads converted and labels came 60 days late. Task: deliver a useful ranking for the sales team. Action: I set up a clean time-based validation, engineered activity features, used gradient boosting with class weights and measured lift in the top decile. Result: the top 10 percent of scored leads converted 3.5 times better than average. The hardest part was building trust in the labels.
09
What are context managers in Python?
Show answer
A context manager sets something up and guarantees cleanup when a block ends, even if an error is raised. You use it with the with statement, for example with open('data.csv') as f, which always closes the file. It works through __enter__ and __exit__ methods, or the contextlib.contextmanager decorator. In data work it is used for files, database connections, locks and timers, so resources are never left open.
10
Why do we use activation functions? Why is ReLU popular?
Show answer
Activations add non-linearity so networks can learn complex patterns. ReLU (max(0, x)) is cheap and reduces vanishing gradients compared with sigmoid/tanh; Leaky ReLU avoids 'dead' neurons.
11
What does CI/CD mean for machine learning projects?
Show answer
Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.
12
What are polynomial features and what is the risk of using them?
Show answer
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.
13
What is the receptive field of a neuron in a CNN?
Show answer
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.
14
Why do residual connections make very deep networks easier to train?
Show answer
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.
15
What is the difference between a base model and an instruction-tuned model?
Show answer
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.
16
Your logging system was down for four days, leaving gaps in a daily series. How do you handle them before modelling?
Show answer
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.
17
What are tokens and the context window in an LLM?
Show answer
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.
18
What is the logical order of execution of a SQL query?
Show answer
Although you write SELECT first, the database logically processes FROM and JOIN first, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because WHERE runs before SELECT, but you can use it in ORDER BY. Window functions are computed after WHERE and GROUP BY, so filter on them in an outer query.
19
What is normalisation, and why are analytics tables often denormalised?
Show answer
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.
20
What is effect size and why should you report it along with a p-value?
Show answer
Effect size measures how large a difference is, for example the absolute lift in conversion or Cohen's d for means. A p-value only says whether the difference is likely due to chance. With millions of users, a 0.01 percent lift can be significant yet useless for the business. Reporting effect size with a confidence interval tells decision makers both whether the effect is real and whether it is worth acting on.
21
How is an image represented for a deep learning model?
Show answer
An image is a numeric array of height by width by channels, where a colour image has three channels, usually RGB, with pixel values from 0 to 255. Libraries differ: OpenCV loads BGR in HWC order, while PyTorch expects tensors in CHW order with a batch dimension in front. Before training you convert to float, scale to 0-1 and normalise with the mean and standard deviation used by the pretrained model.
22
Your fraud model has 99 percent accuracy, but the business says it catches almost no fraud. Why?
Show answer
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.
23
Write a Dockerfile for a Python model API.
Show answer
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app/ ./app
COPY model.joblib .
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
24
What is tokenisation in NLP?
Show answer
Splitting text into units such as words, subwords or characters that are mapped to ids; subword methods like BPE handle rare words well.
25
Pandas: find the top 3 products by revenue in each region.
Show answer
(df.assign(rev=df.price * df.qty)
.groupby(['region', 'product'])['rev'].sum()
.reset_index()
.sort_values('rev', ascending=False)
.groupby('region').head(3))
26
Explain the KNN algorithm and its drawbacks.
Show answer
It predicts from the k nearest labelled points. It is simple but slow at prediction time, sensitive to scaling and irrelevant features, and weak in high dimensions.
27
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
Show answer
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
28
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
Show answer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
pre = ColumnTransformer([
('num', make_pipeline(SimpleImputer(strategy='median'), StandardScaler()), num_cols),
('cat', make_pipeline(SimpleImputer(strategy='most_frequent'),
OneHotEncoder(handle_unknown='ignore')), cat_cols)
])
model = make_pipeline(pre, LogisticRegression(max_iter=1000))
29
What is the difference between ROW_NUMBER, RANK and DENSE_RANK?
Show answer
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.
30
Your classifier scores 98 percent on validation, but Grad-CAM shows it focuses on a watermark rather than the object. What is happening?
Show answer
The model has learned a shortcut: the watermark correlates with the label in your dataset, perhaps because images of one class came from a single source. It will fail when that correlation breaks. Remove or crop the watermarks, rebalance sources across classes, and create a validation set from different sources. Keep using saliency maps and slice-based evaluation to catch other spurious cues such as backgrounds or text.
31
What is the minimum detectable effect (MDE) in experiment design?
Show answer
The MDE is the smallest true effect your test can reliably detect given the sample size, baseline rate, significance level and power. If baseline conversion is 5 percent and your MDE is 10 percent relative, you can detect a move to 5.5 percent but will likely miss smaller lifts. Choose the MDE from business value: the smallest change worth shipping. A smaller MDE needs a much larger sample, roughly growing with one over MDE squared.
32
Your product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?
Show answer
This is domain shift: user photos have clutter, blur, poor lighting and odd angles that the model never saw. Collect and label a sample of real user photos and evaluate on them separately. Add augmentation for blur, noise, colour jitter, rotation and random crops, and fine-tune on a mix of studio and real images. Monitor accuracy on user photos in production, since that is the distribution that matters.
33
What is the difference between image classification, object detection and image segmentation?
Show answer
Classification assigns one label to the whole image, such as cat. Object detection finds each object and returns a bounding box plus a class label and confidence for each one. Segmentation labels every pixel, giving exact object shapes. The tasks need increasingly detailed annotations and compute. Choose based on the business need: counting products on a shelf needs detection, while measuring a crack's area needs segmentation.
34
What is pooling in a CNN and why is it used?
Show answer
Pooling downsamples a feature map by summarising small regions. Max pooling keeps the largest value in each window, such as 2x2, and average pooling keeps the mean. It reduces computation, enlarges the receptive field of later layers and gives a little tolerance to small shifts. Many modern networks replace pooling with strided convolutions, but global average pooling before the classifier head is still very common.
35
What is the Central Limit Theorem?
Show answer
The distribution of sample means approaches a normal distribution as the sample size grows (roughly 30+), whatever the population's shape. It lets us build confidence intervals and run hypothesis tests on means.
36
A 10 million row DataFrame with a city column is using too much memory. How can the category dtype help?
Show answer
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.
37
What is an Overall Evaluation Criterion (OEC) in experimentation?
Show answer
An OEC is a single agreed metric, or a weighted combination of metrics, that represents long-term business value and is used to judge experiments. Good OECs are sensitive enough to move within a test's duration yet aligned with long-term goals; for example, sessions per user with penalties for churn rather than raw clicks. Having an OEC prevents teams from each optimising their own metric and makes ship decisions consistent across the organisation.
38
What is method chaining in Pandas and why do teams use it?
Show answer
Method chaining writes a sequence of transformations as one expression, for example df.query(...).assign(...).groupby(...).agg(...). It avoids many temporary variables, reads top to bottom like a recipe, and reduces the risk of changing the wrong intermediate DataFrame. assign and pipe make chaining easy. The trade-off is that long chains are harder to debug, so split them into named steps when a chain grows past eight or ten operations.
39
Write scikit-learn code to get precision, recall and a confusion matrix at a custom threshold of 0.3.
Show answer
from sklearn.metrics import confusion_matrix, precision_score, recall_score
proba = model.predict_proba(X_test)[:, 1]
pred = (proba >= 0.3).astype(int)
print(confusion_matrix(y_test, pred))
print('precision', precision_score(y_test, pred))
print('recall', recall_score(y_test, pred))
40
What is a t-test and when do you use a paired t-test?
Show answer
A t-test compares means when the population variance is unknown. A two-sample t-test compares two independent groups, such as users in control and treatment. A paired t-test is used when the same units are measured twice, such as the same students before and after training. Pairing removes variation between individuals, so it is more powerful. Welch's t-test is preferred when the two groups have unequal variances.
Tips to crack the Birlasoft Data Science interview
- Learn basic ERP concepts such as procure-to-pay and order-to-cash, as many Birlasoft projects involve enterprise applications.
- Revise SQL and data basics, since ERP and analytics roles both rely on strong querying skills.
- Read about the CK Birla Group and Birlasoft's manufacturing and life sciences client focus.
- 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; Birlasoft interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Birlasoft Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Technical MCQs; Technical interview; 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 Birlasoft Data Science interview?
This practice set leans on Computer Vision (8), Statistics and Probability (6), Deep Learning and NLP (4), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Birlasoft interviewers ask you to walk through real work.
Are these the exact questions asked at Birlasoft?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Birlasoft's. They are not a leaked or verified list of past questions.
Where does Birlasoft hire Data Science professionals in India?
Birlasoft has large teams in Pune, Noida, Bengaluru, Chennai. Openings and locations change often, so check the Birlasoft careers page and filter by city and experience.
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