L&T Technology Services Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About L&T Technology Services hiring for Data Science roles
L&T Technology Services is an engineering research and development services company and part of the Larsen & Toubro group. It works with clients in transportation, industrial products, telecom, medical devices and plant engineering on product design, embedded systems and digital engineering. In India it hires engineers for software, embedded, testing and data roles across its engineering and delivery centres.
For Data Science roles, this L&T Technology Services practice set focuses most on Statistics and Probability (8), Deep Learning and NLP (7), Python for Data Science (6), MLOps and Model Deployment (5). It has 23 easy, 16 medium, 1 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Vadodara, India
Main India offices
Vadodara, Bengaluru, Chennai, Mysuru, Pune
L&T Technology Services Data Science interview process
Online assessment (aptitude + technical MCQs)
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 product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?
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.
Binning groups a continuous variable into ranges, like age into 18-25, 26-35 and so on. It can capture non-linear effects in linear models, reduce the impact of outliers and make results easier to explain to business teams. Bins can be equal width, equal frequency using quantiles, or chosen by domain knowledge. The downside is losing information inside each bin, and tree models usually do not need it.
Covariance shows the direction of a relationship but depends on units. Correlation is covariance scaled to −1…+1, so it shows direction and strength and is unit-free.
A DAG, directed acyclic graph, in Apache Airflow defines a workflow as tasks with dependencies, such as extract data, build features, train model and publish scores, in that order. Airflow schedules runs, retries failed tasks, passes run dates and shows status in a web UI. Acyclic means no task can loop back to an earlier one. Data scientists use it for scheduled training and batch scoring pipelines.
05
Write Python code using Hugging Face transformers to run sentiment analysis on a list of reviews.
from transformers import pipeline
clf = pipeline(
'sentiment-analysis',
model='distilbert-base-uncased-finetuned-sst-2-english'
)
reviews = ['Delivery was fast and the product is great',
'Packaging was damaged and support did not reply']
for text, out in zip(reviews, clf(reviews, batch_size=16)):
print(out['label'], round(out['score'], 3), '-', text)
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.
MLE chooses model parameters that make the observed data most probable. You write the likelihood of the data as a function of the parameters, usually take the log for easier maths, and maximise it. For a coin with 7 heads in 10 tosses, the MLE of the heads probability is 0.7. Logistic regression is trained by MLE, which is the same as minimising log loss. MLE can overfit with small samples.
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.
09
Why do you apply a log transform to some features?
A log transform compresses large values and spreads small ones, which reduces right skew in features like income, price or page views. This helps linear models and distance-based models that are sensitive to scale and outliers. Use log1p, which is log of 1 plus x, when the feature has zeros. For negative values, a Yeo-Johnson transform works. Tree models generally do not need it.
The ACF plot shows correlation between the series and its own lags, including indirect effects through intermediate lags. The PACF shows the correlation at each lag after removing the effect of shorter lags. For a stationary series, a PACF that cuts off after lag p suggests an AR(p) term, and an ACF that cuts off after lag q suggests an MA(q) term. Spikes at lag 7 or 12 reveal weekly or yearly seasonality.
A measure of asymmetry. Right-skewed data has a long tail on the right (mean greater than median); left-skewed is the opposite. Log transforms often reduce right skew.
13
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
A two-tailed test checks for a difference in either direction, for example whether a new page changes conversion up or down. A one-tailed test checks only one direction, such as whether it increases conversion. One-tailed tests have more power for that direction but ignore harm in the other direction. In most product experiments a two-tailed test is the safer default unless the direction was fixed before seeing data.
15
Write SQL to compute a 7-day rolling average of daily active users.
WITH daily AS (
SELECT activity_date, COUNT(DISTINCT user_id) AS dau
FROM events
GROUP BY activity_date
)
SELECT activity_date, dau,
AVG(dau) OVER (ORDER BY activity_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS dau_7d_avg
FROM daily
ORDER BY activity_date;
During training it randomly switches off a fraction of neurons so the network cannot depend on any single unit, acting as a regulariser against overfitting.
17
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]
18
What is method chaining in Pandas and why do teams use it?
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.
MLOps is the set of practices that take machine learning models from experiments to reliable production and keep them working. It covers versioning data and code, tracking experiments, automated training pipelines, testing, deployment, monitoring and retraining. It borrows ideas from DevOps but adds data and model concerns, because a model can fail even when the code has not changed, simply because the data has.
20
What should you log for a production prediction service?
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.
21
How do you pull large data from a SQL database into Pandas efficiently?
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
22
What is the difference between layer normalisation and batch normalisation?
Batch normalisation normalises each feature using the mean and variance across the batch, so it depends on batch size and behaves differently in training and inference. Layer normalisation normalises across the features of each single example, so it works with any batch size and with variable-length sequences. That is why transformers use layer norm, often in the pre-norm position, while CNNs traditionally use batch norm.
23
What is experiment tracking and why use a tool like MLflow?
Experiment tracking records each training run's parameters, code version, data version, metrics and output artefacts. Tools like MLflow or Weights and Biases store this in one place, so you can compare runs, reproduce the best model and see why something improved. Without it, results live in notebook outputs and memory. MLflow also offers a model registry to move approved models through staging and production.
24
How would you explain a statistical concept to a non-technical manager?
I start with the decision they care about, not the formula. Once a sales manager asked why a 3 percent lift was not significant. I explained that with only 200 customers, the result could easily happen by chance, like getting 6 heads in 10 coin tosses. I showed a simple range chart of the confidence interval and suggested running the test longer. He agreed, and the extended test gave a clear answer.
25
Write Python code to count word frequencies in a text and print the top 5 words.
from collections import Counter
import re
def top_words(text, n=5):
words = re.findall(r'[a-z]+', text.lower())
return Counter(words).most_common(n)
print(top_words('Data science is fun and data is everywhere'))
Recursive feature elimination trains a model, ranks features by importance or coefficient size, removes the weakest ones, and repeats until the desired number remains. RFECV picks the number of features using cross-validation. It considers features together rather than one at a time. The downsides are high compute cost on large data and unstable results when features are correlated, so check stability across folds.
27
What is the difference between a subquery and a correlated subquery?
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.
SGD updates weights using the gradient times a fixed learning rate, often with momentum. Adam keeps running averages of gradients and their squares, giving each parameter its own adaptive step size. Adam converges fast with little tuning and is the default for transformers. Well-tuned SGD with momentum sometimes generalises better on vision tasks. AdamW, which decouples weight decay, is the common choice for modern deep learning.
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.
31
How would you quickly build a sentiment analysis model for product reviews?
Step 1: collect and label a sample of reviews, checking class balance. Step 2: build a baseline with TF-IDF and logistic regression to get a quick benchmark. Step 3: fine-tune a small pretrained transformer such as DistilBERT for better accuracy, or use a multilingual model if reviews mix languages. Step 4: evaluate with F1 per class and read wrong predictions. Step 5: handle sarcasm and neutral reviews explicitly.
32
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.
Split the data into k folds, train on k−1 folds and validate on the remaining one, rotate and average the scores. It gives a more reliable estimate than one split and helps tune hyperparameters without touching the test set.
34
What is stratified sampling in a train-test split?
Stratified splitting keeps the class proportions the same in the training and test sets. If 5 percent of customers churn, both sets will have about 5 percent churners. Without it, a small or imbalanced dataset can give a test set with very few positives and unstable metrics. In scikit-learn you pass stratify=y to train_test_split or use StratifiedKFold for cross-validation.
35
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.
36
Pandas: convert wide monthly columns into a long format table.
Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.
39
What is the difference between batch and real-time inference?
Batch inference scores many records on a schedule, for example nightly churn scores for all customers written to a table. It is cheap and simple. Real-time inference serves predictions on request through an API within milliseconds, for example fraud checks during payment. It needs low-latency feature access, scaling and high availability. Choose real-time only when the decision truly must use fresh data at the moment of the request.
40
Write Python code for a two-proportion z-test comparing conversion between control and treatment.
Tips to crack the L&T Technology Services Data Science interview
Revise core engineering subjects from your branch along with C, basic data structures and one scripting language before the technical round.
Be ready to explain an academic or internship project end to end, including design choices, tools used and testing approach.
Read about LTTS segments like mobility, medical devices and plant engineering so you can connect your skills to a domain.
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; L&T Technology Services interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the L&T Technology Services Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical MCQs); 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 L&T Technology Services Data Science interview?
This practice set leans on Statistics and Probability (8), Deep Learning and NLP (7), Python for Data Science (6), MLOps and Model Deployment (5). Revise those first, then practise explaining one or two of your own projects end to end, because most L&T Technology Services interviewers ask you to walk through real work.
Are these the exact questions asked at L&T Technology Services?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like L&T Technology Services's. They are not a leaked or verified list of past questions.
Where does L&T Technology Services hire Data Science professionals in India?
L&T Technology Services has large teams in Vadodara, Bengaluru, Chennai, Mysuru, Pune. Openings and locations change often, so check the L&T Technology Services careers page and filter by city and experience.