01
What are lambda functions and where are they useful?
Show answer
Small anonymous one-line functions, handy as arguments to sort, map, filter or Pandas apply.
NTT DATA Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About NTT DATA hiring for Data Science roles
NTT DATA is a global IT services company and part of the NTT Group of Japan. It provides consulting, application services, cloud, infrastructure, data and testing services for clients in many industries. Its India delivery centres hire for software engineers, test engineers, data engineers, data analysts and infrastructure professionals.
For Data Science roles, this NTT DATA practice set focuses most on Python for Data Science (7), Machine Learning (6), Computer Vision (5), SQL for Data Science (4). It has 20 easy, 20 medium questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Tokyo, Japan
- Main India offices
- Bengaluru, Pune, Noida, Hyderabad, Chennai
NTT DATA 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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02
What is a baseline model and why should you build one first?
Show answer
A baseline is the simplest reasonable prediction, such as predicting the most common class, last week's value or a basic logistic regression. It sets the minimum score any real model must beat. Without it, a 75 percent accuracy can look good when predicting the majority class already gives 80 percent. Baselines also catch pipeline bugs early and show whether extra complexity is worth it.
03
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.
04
Write OpenCV code to read an image, convert it to grayscale, resize it and detect edges.
Show answer
import cv2
img = cv2.imread('part.jpg') # loaded as BGR
if img is None:
raise FileNotFoundError('part.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
small = cv2.resize(gray, (224, 224), interpolation=cv2.INTER_AREA)
blur = cv2.GaussianBlur(small, (5, 5), 0)
edges = cv2.Canny(blur, 50, 150)
cv2.imwrite('edges.png', edges)
05
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.
06
Explain precision vs recall. When would you prefer recall?
Show answer
Precision = TP / (TP + FP). Recall = TP / (TP + FN).
Prefer recall when missing a positive is costly — for example disease screening or fraud detection.
07
You need to extract fields from scanned invoices that come in hundreds of different layouts. How would you design it?
Show answer
Fixed templates will not scale to hundreds of layouts. Start with preprocessing: deskew, denoise and detect text regions. Run OCR to get words with their positions. Then use a layout-aware document model, or a vision-language model, to map words to fields like invoice number, GSTIN and total. Add validation rules, such as GSTIN format and line items summing to the total, and send low-confidence documents to human review.
08
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.
09
Pandas: compute a 7-day rolling average of daily sales for each store.
Show answer
# df has columns: store, date, sales
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(['store', 'date'])
df['sales_7d_avg'] = (
df.groupby('store')['sales']
.transform(lambda s: s.rolling(7, min_periods=1).mean())
)
10
What is Bayesian A/B testing and how are its results reported?
Show answer
Bayesian A/B testing starts with a prior on each variant's metric, updates it with observed data and produces a posterior distribution. Instead of a p-value, you report things like the probability that treatment beats control, say 94 percent, and the expected loss if you choose the wrong variant. Business teams find this easier to interpret. Priors must be chosen sensibly, and continuous monitoring still needs discipline about decision rules.
11
Tell me about a time you explained the limits of GenAI to business stakeholders.
Show answer
Situation: sales leaders wanted an LLM to auto-send pricing quotes to clients. Task: guide them to a safe plan. Action: I demoed real failure cases where the model invented discounts, showed accuracy numbers from a test set and proposed a draft-and-approve flow with fixed price data from our system. Result: they agreed, quote drafting time fell by half, and no wrong prices reached clients.
12
Write scikit-learn code for a pipeline with scaling and logistic regression, evaluated with 5-fold cross-validation.
Show answer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
scores = cross_val_score(pipe, X, y, cv=5, scoring='roc_auc')
print(scores.mean(), scores.std())
13
What is PCA and when do you use it?
Show answer
It projects data onto orthogonal directions of maximum variance to reduce dimensions, remove correlation and speed up models, at some cost of interpretability.
14
What is cross-validation and why use it?
Show answer
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.
15
What are primary metrics and guardrail metrics in an experiment?
Show answer
The primary metric is the one the change is meant to improve and that decides success, such as checkout conversion. Guardrail metrics are ones that must not get worse, such as page load time, refund rate, unsubscribes or revenue per user. Defining both before the test prevents cherry-picking afterwards. A change that lifts the primary metric but breaks a guardrail is usually not shipped without further discussion.
16
Write Pandas code to read a large CSV in chunks and compute total sales per city.
Show answer
# process 1 million rows at a time
import pandas as pd
totals = {}
for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000):
part = chunk.groupby('city')['amount'].sum()
for city, amt in part.items():
totals[city] = totals.get(city, 0) + amt
result = pd.Series(totals).sort_values(ascending=False)
17
Explain exponential smoothing and the Holt-Winters method.
Show answer
Simple exponential smoothing forecasts with a weighted average of past values where weights decay exponentially, so recent points matter more. Holt's method adds a smoothed trend component. Holt-Winters adds a seasonal component as well, either additive or multiplicative. Each component has a smoothing parameter between 0 and 1 that is fitted to the data. These models are fast, easy to explain and strong baselines for retail and demand data.
18
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.
19
What is U-Net and why is it popular for segmentation?
Show answer
U-Net is an encoder-decoder network. The encoder downsamples to capture context, and the decoder upsamples back to full resolution to produce a per-pixel mask. Skip connections copy encoder feature maps to the matching decoder level, so fine spatial detail lost during downsampling is recovered. It works well with limited labelled data, which made it the standard for medical imaging and industrial defect segmentation, and it is also the backbone of many diffusion models.
20
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.
21
What is the difference between WHERE and HAVING in SQL?
Show answer
WHERE filters individual rows before grouping, so it cannot use aggregate functions. HAVING filters groups after GROUP BY and can use aggregates. Example: WHERE order_date >= '2026-01-01' keeps only this year's orders, then GROUP BY customer_id HAVING SUM(amount) > 10000 keeps customers who spent more than 10,000. Filtering early with WHERE is also faster because fewer rows are grouped.
22
What is batch normalisation?
Show answer
It normalises layer inputs per mini-batch and then rescales them, stabilising and speeding up training and allowing higher learning rates.
23
What is a system prompt in an LLM application?
Show answer
A system prompt is the instruction message placed before user messages that sets the model's role, tone, rules and output format, for example 'You are a support assistant; answer only from the provided policy and reply in JSON'. It shapes every response in the conversation. Keep it clear and specific, version it like code, and do not rely on it alone for security, since users can try to override it through prompt injection.
24
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.
25
What is data augmentation?
Show answer
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.
26
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.
27
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).
28
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.
29
What is Naive Bayes and why is it called naive?
Show answer
It applies Bayes' theorem assuming features are independent given the class. The assumption is rarely true but the model is fast and works well for text.
30
Write SQL to compute a 7-day rolling average of daily active users.
Show answer
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;
31
What does the axis parameter mean in NumPy and Pandas?
Show answer
axis tells an operation which direction to work along. axis=0 works down the rows, so df.sum(axis=0) gives one total per column. axis=1 works across columns, so df.sum(axis=1) gives one total per row. In df.drop, axis=1 means drop columns and axis=0 means drop rows. A simple way to remember it: the axis you name is the one that gets collapsed or removed.
32
What is a time series and how is it different from ordinary tabular data?
Show answer
A time series is a sequence of observations recorded at ordered time steps, such as daily sales or hourly website traffic. Unlike ordinary tabular data, rows are not independent: today's value depends on yesterday's, and the order cannot be shuffled. This changes everything downstream. You must split train and test by time, build features only from the past, and check for trend and seasonality before choosing a model.
33
What are dataclasses in Python and why are they useful in data projects?
Show answer
A dataclass, created with the @dataclass decorator, automatically generates __init__, __repr__ and __eq__ from annotated fields. It is a clean way to hold structured records such as a training config with learning rate, batch size and paths. Options like frozen=True make instances immutable, and default values reduce boilerplate. Compared with plain dictionaries, dataclasses give attribute access, type hints and clearer errors when a field is misspelled.
34
What is experiment tracking and why use a tool like MLflow?
Show answer
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.
35
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.
36
Tell me about a time you disagreed with a stakeholder about which metric to optimise.
Show answer
Situation: marketing wanted the lead model judged on overall accuracy. Task: agree on a metric that matched the goal. Action: I showed that accuracy rewarded predicting no conversion for everyone, then proposed precision in the top 1,000 leads per week, which matched the sales team's call capacity, with a simple chart. Result: they agreed, and the model with the better top-1,000 precision added 18 percent more conversions.
37
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.
38
What is the difference between UNION and UNION ALL?
Show answer
Both stack the results of two queries with the same number and type of columns. UNION removes duplicate rows, which needs an extra sort or hash step and is slower. UNION ALL keeps every row, including duplicates, and is faster. Use UNION ALL by default when you know the sets do not overlap or when duplicates matter, such as combining transaction logs from two systems.
39
What is the binomial distribution and when do you use it?
Show answer
The binomial distribution gives the probability of getting k successes in n independent trials, each with the same success probability p. Its mean is n times p and its variance is n times p times (1 minus p). You use it for counts of yes-or-no outcomes, such as how many of 1,000 visitors convert or how many of 50 parts are defective. For large n it is well approximated by a normal distribution.
40
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.
Tips to crack the NTT DATA Data Science interview
- Revise SQL, testing and programming fundamentals for both the online assessment and technical interview.
- Learn about NTT DATA's Japanese parent and its global integration of companies such as Dimension Data.
- Be ready to discuss working with clients across time zones, as many teams support global accounts.
- 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; NTT DATA interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the NTT DATA 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 NTT DATA Data Science interview?
This practice set leans on Python for Data Science (7), Machine Learning (6), Computer Vision (5), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most NTT DATA interviewers ask you to walk through real work.
Are these the exact questions asked at NTT DATA?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like NTT DATA's. They are not a leaked or verified list of past questions.
Where does NTT DATA hire Data Science professionals in India?
NTT DATA has large teams in Bengaluru, Pune, Noida, Hyderabad, Chennai. Openings and locations change often, so check the NTT DATA careers page and filter by city and experience.
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