01
Random forest vs a single decision tree?
Show answer
A forest averages many trees trained on bootstrapped rows and random feature subsets, which cuts variance and overfitting at the cost of interpretability.
LTIMindtree Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About LTIMindtree hiring for Data Science roles
LTIMindtree is an Indian IT services and consulting company formed by the merger of Larsen and Toubro Infotech and Mindtree, and part of the L&T Group. It provides cloud, data, ERP, testing and digital engineering services for global clients. It hires for software engineers, test engineers, data analysts, data engineers and ERP consultants.
For Data Science roles, this LTIMindtree practice set focuses most on Statistics and Probability (6), Python for Data Science (6), Deep Learning and NLP (6), SQL for Data Science (5). 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
- Mumbai, India
- Main India offices
- Mumbai, Pune, Bengaluru, Chennai, Hyderabad
LTIMindtree Data Science interview process
- Online assessment (aptitude + reasoning)
- Coding test
- 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 the difference between a one-tailed and a two-tailed test?
Show answer
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.
03
Write Pandas code to cap outliers at the 1st and 99th percentile.
Show answer
# 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)
04
Pandas: merge vs join vs concat?
Show answer
merge joins on columns, SQL-style (how='inner', 'left', ...). join joins on the index. concat stacks DataFrames along rows or columns.
05
What are the novelty effect and the primacy effect in A/B tests?
Show answer
The novelty effect is when users engage more with a change simply because it is new, so early results overstate the long-term lift. The primacy effect is the opposite: existing users resist change at first, so early results understate it. Both fade with time. To detect them, plot the treatment effect by day and by user tenure, compare new users with existing users, and run the test long enough for behaviour to settle.
06
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.
07
What are interaction features and when do they help?
Show answer
Interaction features combine two or more variables, such as price times quantity, or income divided by number of dependents. They help linear models capture effects where one variable changes the impact of another. Tree models can learn some interactions themselves, but explicit ratios often still help. Too many automatic interactions explode the feature count and overfit, so build them from domain logic and validate each one.
08
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.
09
What is backpropagation?
Show answer
It computes the gradient of the loss with respect to every weight by applying the chain rule backwards through the network, so gradient descent can update them.
10
What is the Global Interpreter Lock (GIL)?
Show answer
A mutex in CPython that lets only one thread execute Python bytecode at a time. Use multiprocessing for CPU-bound work and threads or asyncio for I/O-bound work.
11
What is overfitting and how do you prevent it?
Show answer
The model memorises noise and fails on new data. Prevent it with more data, simpler models, regularisation, cross-validation, early stopping and dropout.
12
Explain INNER, LEFT, RIGHT and FULL OUTER joins.
Show answer
An INNER JOIN returns only rows with matching keys in both tables. A LEFT JOIN returns all rows from the left table, with NULLs where the right has no match. A RIGHT JOIN is the mirror of that. A FULL OUTER JOIN returns all rows from both sides, matched where possible. In analytics, LEFT JOIN from a customers table is common so customers with no orders are still counted.
13
What is the bias–variance trade-off?
Show answer
Bias is error from overly simple assumptions (underfitting). Variance is sensitivity to the training data (overfitting). More model complexity lowers bias but raises variance — aim for the lowest total error using cross-validation and regularization.
14
Tell me about a time you automated a manual step in a machine learning workflow.
Show answer
Situation: each monthly retrain needed a day of manual data pulls, notebook runs and copying files to servers. Task: make it reliable and hands-off. Action: I wrapped the steps into a scheduled pipeline with data validation checks, logged runs in MLflow and added an approval step before promotion. Result: retraining time dropped from a day to under an hour of review, and there were no more failed deployments from missed steps.
15
What do kernel size, stride and padding mean in a convolution layer?
Show answer
The kernel is a small learnable filter, such as 3x3, that slides over the image and computes a weighted sum at each position. Stride is how many pixels it moves each step; stride 2 halves the output size. Padding adds border pixels so the output can keep the same size and edge pixels get processed. Output size equals (input minus kernel plus 2 times padding) divided by stride, plus one.
16
What is the difference between mean, median and mode?
Show answer
Mean is the average, median is the middle value after sorting and mode is the most frequent value. Median is more robust to outliers.
17
Write pytest unit tests for a feature function that computes days since last purchase.
Show answer
import pandas as pd
from features import days_since_last_purchase
def test_basic_case():
df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.loc[1] == 5
def test_no_future_orders_used():
df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.empty
18
What is quantisation of LLMs?
Show answer
Quantisation stores model weights in lower precision, such as 8-bit or 4-bit integers instead of 16-bit floats. A 7-billion parameter model drops from about 14 GB to around 4 GB at 4-bit, so it fits on smaller GPUs or even laptops and runs faster. The cost is some loss in quality, usually small at 8-bit and noticeable on hard tasks at very low bits. Common formats include GPTQ, AWQ and GGUF.
19
What is stationarity and why do many forecasting models need it?
Show answer
A stationary series has a constant mean, constant variance and an autocorrelation structure that does not change over time. Classical models like ARMA assume stationarity because they learn fixed relationships between past and present values; if the mean keeps drifting, those relationships break. You usually make a series stationary by differencing to remove trend, seasonal differencing to remove seasonality, and a log transform to stabilise growing variance.
20
What is recursive feature elimination?
Show answer
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.
21
What is the difference between SGD and Adam optimisers?
Show answer
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.
22
Difference between loc and iloc in Pandas?
Show answer
loc selects by label (row and column names, inclusive of the end label); iloc selects by integer position (end-exclusive).
23
What is the difference between a star schema and a snowflake schema?
Show answer
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.
24
Explain the Law of Large Numbers.
Show answer
As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.
25
What is mean Average Precision (mAP) in object detection?
Show answer
For each class, detections are ranked by confidence and a precision-recall curve is computed, where a detection is a true positive if its IoU with an unmatched ground truth box passes a threshold. Average Precision is the area under that curve. mAP is the mean across classes. mAP@0.5 uses IoU 0.5, while mAP@0.5:0.95 averages over thresholds from 0.5 to 0.95 and rewards tighter boxes.
26
Tell me about a time you had to deliver data under an urgent deadline.
Show answer
Situation: the leadership team needed festive-sale numbers by city for a 9 am review, and the dashboard pipeline had failed overnight. Task: deliver accurate numbers in three hours. Action: I queried the raw orders tables directly, reused validated logic from the dashboard SQL, cross-checked totals with finance's daily figure and flagged two cities with partial data. Result: the review went ahead on time, and I later added pipeline failure alerts.
27
Training a transformer on a GPU fails with an out-of-memory error. What can you do?
Show answer
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.
28
How do one-stage and two-stage object detectors differ?
Show answer
Two-stage detectors, like Faster R-CNN, first propose candidate regions and then classify and refine each one, which gives strong accuracy, especially for small objects, but is slower. One-stage detectors, like YOLO and SSD, predict boxes and classes directly over a dense grid in a single pass, making them fast enough for real-time video. Modern one-stage models have closed most of the accuracy gap, so they dominate edge and video use cases.
29
Type I vs Type II error?
Show answer
Type I = false positive (rejecting a true null), probability α. Type II = false negative (missing a real effect), probability β. Power = 1 − β.
30
What is the difference between zero-shot and few-shot prompting?
Show answer
In zero-shot prompting you give only instructions, like classify this review as positive or negative. In few-shot prompting you also include a few worked examples of input and correct output, which shows the model the format and decision boundary. Few-shot usually improves consistency on custom labels, at the cost of more tokens. Pick examples that cover tricky cases and keep their format identical to what you want back.
31
Write SQL to compute week-1 retention by signup week.
Show answer
WITH signups AS (
SELECT user_id, DATE_TRUNC('week', signup_date) AS cohort, signup_date
FROM users
), active AS (
SELECT DISTINCT s.user_id, s.cohort
FROM signups s
JOIN events e ON e.user_id = s.user_id
AND e.event_date >= s.signup_date + INTERVAL '7 day'
AND e.event_date < s.signup_date + INTERVAL '14 day'
)
SELECT s.cohort, COUNT(*) AS users,
ROUND(100.0 * COUNT(a.user_id) / COUNT(*), 1) AS week1_retention_pct
FROM signups s
LEFT JOIN active a ON a.user_id = s.user_id
GROUP BY s.cohort
ORDER BY s.cohort;
32
A Pandas merge suddenly returns many more rows than the left table had. What happened?
Show answer
The join key is probably not unique on the right side, so each left row matches several right rows and the result multiplies. Check with right['key'].duplicated().sum(). Other causes are null or blank keys matching each other after cleaning, or joining on the wrong column. Fix it by deduplicating or aggregating the right table first, and use merge(..., validate='many_to_one') so Pandas raises an error when the assumption breaks.
33
What is NumPy broadcasting?
Show answer
The rule that lets arrays of different shapes operate together by virtually stretching the smaller one, for example adding a scalar or a (3,) vector to a (4,3) matrix.
34
What is gradient descent and what does the learning rate do?
Show answer
An optimisation method that updates weights in the opposite direction of the loss gradient. The learning rate sets the step size: too high and it diverges, too low and it is slow. Variants include SGD, mini-batch and Adam.
35
What is an autoencoder and what is it used for?
Show answer
A network that compresses inputs into a small latent code and reconstructs them; used for denoising, dimensionality reduction and anomaly detection.
36
What is transfer learning?
Show answer
Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.
37
What is the attention mechanism?
Show answer
It lets a model weigh which input positions matter most for each output, computing weighted combinations of values from query-key similarity. It is the core of transformers.
38
How do list comprehensions work and why use them?
Show answer
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.
39
What is standard deviation and what does it tell you?
Show answer
The square root of the variance; it measures how far values typically sit from the mean, in the same units as the data.
40
What is Intersection over Union (IoU)?
Show answer
IoU measures how well a predicted box or mask overlaps the ground truth. It is the area of their intersection divided by the area of their union, giving a value from 0 for no overlap to 1 for a perfect match. In detection, a prediction usually counts as correct if IoU is at least 0.5 with a ground truth box of the same class. IoU is also used inside non-maximum suppression.
Tips to crack the LTIMindtree Data Science interview
- Practise aptitude, logical reasoning and basic coding questions, as the online assessment is usually the first filter.
- Revise SQL, OOP and testing fundamentals, plus any cloud or data tools you list on your resume.
- Learn about the L&T Group background and the LTI and Mindtree merger to answer company-related questions.
- 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; LTIMindtree interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the LTIMindtree Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Coding test; 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 LTIMindtree Data Science interview?
This practice set leans on Statistics and Probability (6), Python for Data Science (6), Deep Learning and NLP (6), SQL for Data Science (5). Revise those first, then practise explaining one or two of your own projects end to end, because most LTIMindtree interviewers ask you to walk through real work.
Are these the exact questions asked at LTIMindtree?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like LTIMindtree's. They are not a leaked or verified list of past questions.
Where does LTIMindtree hire Data Science professionals in India?
LTIMindtree has large teams in Mumbai, Pune, Bengaluru, Chennai, Hyderabad. Openings and locations change often, so check the LTIMindtree careers page and filter by city and experience.
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