Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Cyient hiring for Data Science roles
Cyient is an Indian engineering and technology solutions company that started in geospatial services and now covers aerospace, rail, communications, medical and utilities engineering. It provides product design, embedded software, network engineering and data services to global manufacturers and operators. In India it hires freshers and experienced engineers for design, software, testing, GIS and analytics roles at its delivery centres.
For Data Science roles, this Cyient practice set focuses most on Deep Learning and NLP (7), Machine Learning (7), Python for Data Science (5), SQL for Data Science (4). It has 15 easy, 23 medium, 2 hard questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Hyderabad, India
Main India offices
Hyderabad, Bengaluru, Noida
Cyient 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
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.
02
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.
03
Write a prompt template for classifying customer support tickets.
System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.
04
What is gradient descent and what does the learning rate do?
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.
05
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.
06
What is a prediction interval and why should a forecast include one?
A prediction interval gives a range in which a future value is expected to fall with a stated probability, such as 80 or 95 percent. A point forecast of 1,000 units means little without knowing whether the realistic range is 950 to 1,050 or 400 to 1,600. Planners use the interval to set safety stock and staffing. Intervals widen with the horizon because uncertainty accumulates over time.
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.
08
What is SARIMA and how does it extend a plain ARIMA model?
SARIMA adds seasonal terms to ARIMA. It is written ARIMA(p,d,q)(P,D,Q)m, where the second set are seasonal AR, differencing and MA orders, and m is the season length, such as 7 for daily data with a weekly cycle or 12 for monthly data. SARIMAX further allows external regressors like price or holidays. It handles one seasonal period well but struggles with multiple seasonalities, such as daily plus yearly.
09
Your NLP classifier works well on English reviews but fails on Hinglish reviews. What do you do?
First measure the gap with a labelled Hinglish test set. Hinglish mixes scripts and spellings, so tokenisers built for English split it badly. I would try a multilingual or Indic model such as mBERT, XLM-R or IndicBERT, normalise common spellings, and add labelled Hinglish data, possibly with transliteration. Evaluate per language segment and monitor the language mix in production, because it changes over time.
10
When would you use MAE, MSE or RMSE for a regression model?
MAE is the average absolute error; it is easy to explain and less sensitive to outliers. MSE squares errors, so large errors are punished heavily, and it is smooth for optimisation. RMSE is the square root of MSE, so it is in the same units as the target while still punishing big misses. Use RMSE when large errors are very costly, like stock-out forecasts, and MAE when outliers should not dominate.
11
What is the difference between a set and a dictionary?
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.
13
How is an image represented for a deep learning model?
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.
14
What is a baseline model and why should you build one first?
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.
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.
17
You have 50,000 unlabelled images and budget to label only 3,000. How do you use the budget well?
Do not pick 3,000 at random. Label a small diverse seed set, using embedding clustering to cover variety, and train a first model. Then use active learning: label the images the model is least confident about or that disagree across models. Use semi-supervised methods like pseudo-labelling high-confidence images, or self-supervised pretraining on all 50,000. Keep a randomly sampled test set so evaluation stays unbiased.
18
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.
19
What is the difference between an epoch, a batch and an iteration?
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.
21
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)
22
What is chunking in RAG and how do you choose a chunk size?
Chunking splits documents into smaller pieces before embedding them, so retrieval returns focused passages that fit in the prompt. Small chunks, like 200 tokens, are precise but lose context; large chunks, like 1,000 tokens, keep context but add noise and cost. Split on natural boundaries such as headings and paragraphs, add some overlap, and keep metadata like title and section. Then tune chunk size using retrieval metrics on real questions.
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.
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.
NULL means unknown, so any comparison with NULL, even NULL = NULL, gives unknown and the row is filtered out. Use IS NULL or IS NOT NULL instead. Aggregates like SUM, AVG and COUNT(column) skip NULLs, so AVG may be computed over fewer rows than you expect. NOT IN with a subquery that contains a NULL returns no rows. COALESCE replaces NULLs with a default value.
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.
29
Why do we use activation functions? Why is ReLU popular?
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.
31
Tell me about a time you found a mistake in your own analysis after sharing it.
Situation: after presenting a retention report, I noticed I had counted test accounts as users. Task: correct it before decisions were made. Action: I reran the numbers that evening, informed my manager and the audience with a short note showing old and new figures and what changed, and added a filter and a check to the shared query. Result: the conclusion held with smaller numbers, and the team valued the transparency.
*args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict. They make function signatures flexible and are common in wrappers and decorators.
Word embeddings represent words as dense vectors, usually 100 to 300 numbers, so that words used in similar contexts get similar vectors. Word2Vec learns them by predicting a word from its neighbours or the neighbours from the word. Unlike one-hot or TF-IDF, embeddings capture meaning, so king and queen are close. Their limit is one vector per word, so bank has the same vector in river bank and bank loan.
34
Write Pandas code to create lag and rolling features for each customer.
df = df.sort_values(['customer_id', 'month'])
g = df.groupby('customer_id')['spend']
df['spend_lag1'] = g.shift(1)
df['spend_lag3'] = g.shift(3)
# shift first so the current month is not included
df['spend_roll3_mean'] = g.transform(lambda s: s.shift(1).rolling(3).mean())
35
Tell me about a time your forecast was badly wrong. How did you handle it?
Situation: my weekly demand forecast under-predicted a category by 30 percent for a month. Task: explain the miss and stop it recurring. Action: I compared forecast and actuals by region and found a new distribution channel had launched that the model knew nothing about. I told the planning team quickly, added channel as a feature and set up a weekly error review. Result: error returned to normal and planners trusted the alerts.
36
What is the difference between a star schema and a snowflake schema?
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.
37
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
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.
In an A/A test both groups get the identical experience. Since there is no real difference, results should be significant only about 5 percent of the time at alpha 0.05. Running A/A tests validates your experimentation platform: it catches broken randomisation, logging bugs, sample ratio problems and metrics whose variance is wrongly estimated. Teams often run them when launching a new platform or a new metric.
40
What is an Overall Evaluation Criterion (OEC) in experimentation?
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.
Tips to crack the Cyient Data Science interview
Revise fundamentals of your engineering branch, since Cyient roles often mix domain engineering knowledge with software or data skills.
Practise basic programming, SQL queries and simple aptitude problems, which commonly feature in the first screening stage.
Learn about Cyient business units such as aerospace, rail and geospatial to explain which area interests you and why.
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; Cyient interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Cyient 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 Cyient Data Science interview?
This practice set leans on Deep Learning and NLP (7), Machine Learning (7), Python for Data Science (5), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Cyient interviewers ask you to walk through real work.
Are these the exact questions asked at Cyient?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Cyient's. They are not a leaked or verified list of past questions.
Where does Cyient hire Data Science professionals in India?
Cyient has large teams in Hyderabad, Bengaluru, Noida. Openings and locations change often, so check the Cyient careers page and filter by city and experience.