Zensar Technologies Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Zensar Technologies hiring for Data Science roles
Zensar Technologies is an Indian IT services company and part of the RPG Group. It provides digital experience, application engineering, testing, cloud and data services to clients in manufacturing, retail, financial services and hi-tech. In India it hires for software engineers, test engineers and data professionals.
For Data Science roles, this Zensar Technologies practice set focuses most on Machine Learning (8), Statistics and Probability (6), Python for Data Science (5), Deep Learning and NLP (4). It has 17 easy, 22 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, Hyderabad, Bengaluru
Zensar Technologies 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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01
What is Bayesian A/B testing and how are its results reported?
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.
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.
03
Your image classifier trained on 2,000 images memorises the training set within 5 epochs. What do you try?
With so little data, I would use transfer learning from a pretrained model like ResNet or EfficientNet and fine-tune only the top layers first. I would add data augmentation such as flips, crops and colour changes, use dropout and weight decay, and apply early stopping on validation loss. Collecting more labelled images or using a smaller model also helps. I would check for duplicate images across train and validation sets too.
04
How do you check whether a variable is normally distributed?
Start visually with a histogram and a Q-Q plot; points close to the straight line suggest normality. Check skewness and kurtosis values. Then use a formal test such as Shapiro-Wilk for small samples or Anderson-Darling. Remember that with very large samples, tests flag tiny harmless deviations, so judge by the plots too. If data is skewed, consider a log transform or a non-parametric method.
05
What is MAPE and when is it a poor metric for forecast accuracy?
MAPE is the mean of absolute errors divided by actual values, expressed as a percentage. It is easy to explain to business teams, but it breaks when actuals are zero or near zero, because the percentage explodes. It also penalises over-forecasts more than under-forecasts and weights small items heavily. For intermittent or low-volume series prefer MAE, weighted MAPE or MASE, which compares error to a naive baseline.
06
A Pandas merge suddenly returns many more rows than the left table had. What happened?
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.
07
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.
08
Tell me about a time your model did not perform as expected after deployment.
Situation: my demand forecasting model had 8 percent error offline, but 20 percent in the first month live. Task: find out why quickly. Action: I compared live inputs with training data and found a promotions feature arrived a day late in production, so the model saw zeros. I added a data freshness check and a fallback. Result: error returned to 9 percent. Since then I always test with production-like pipelines before launch.
09
What do kernel size, stride and padding mean in a convolution layer?
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.
10
Write Python code to group a list of order dictionaries by city and sum the amounts without Pandas.
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.
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.
It projects data onto orthogonal directions of maximum variance to reduce dimensions, remove correlation and speed up models, at some cost of interpretability.
16
What is the difference between semantic, instance and panoptic segmentation?
Semantic segmentation gives each pixel a class, such as road or car, but does not separate two cars. Instance segmentation separates individual objects of countable classes, giving each car its own mask. Panoptic segmentation combines both: every pixel gets a class, and countable objects also get an instance ID, while background stuff like sky stays semantic. Autonomous driving and robotics often need panoptic or instance output.
17
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
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.
19
What is the difference between stemming and lemmatisation?
Both reduce words to a base form. Stemming chops suffixes using rules, so studies becomes studi; it is fast but can produce non-words. Lemmatisation uses vocabulary and grammar to return the real dictionary form, so studies becomes study and better becomes good. Lemmatisation is more accurate but slower. With modern transformer models, neither is usually needed because subword tokenisers handle word forms.
20
What is non-maximum suppression in object detection?
A detector usually predicts many overlapping boxes for the same object. Non-maximum suppression keeps the box with the highest confidence, removes other boxes of the same class whose IoU with it exceeds a threshold such as 0.5, and repeats with the remaining boxes. A threshold that is too low can delete genuinely nearby objects in crowded scenes, so variants like soft-NMS lower scores instead of removing boxes.
21
Write Pandas code to resample daily sales into weekly totals and monthly averages.
A population is the full group you want to learn about, such as all customers of an app. A sample is the subset you actually observe, such as 5,000 surveyed customers. Numbers from the population are parameters, and numbers from a sample are statistics that estimate them. Good sampling, ideally random, lets you generalise from the sample to the population and measure the uncertainty using standard errors and confidence intervals.
24
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.
ARIMA models a time series using three parts. AR, autoregressive, uses past values of the series. I, integrated, differences the series d times to remove trend and make it stationary. MA, moving average, uses past forecast errors. You choose p, d and q using ACF and PACF plots or information criteria like AIC. SARIMA adds seasonal terms. ARIMA works well for single, fairly stable series but struggles with many external drivers.
26
How do you deal with high-cardinality categorical features?
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.
Term Frequency × Inverse Document Frequency: a word scores high if it is frequent in one document but rare across all documents. A classic way to turn text into numeric features.
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.
30
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
Bias occurs when the sample does not represent the population. Reduce it with random or stratified sampling, higher response rates and by checking sample demographics against the population.
32
What does CI/CD mean for machine learning projects?
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.
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.
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.
An AI agent is an LLM that works in a loop: it plans a step, calls a tool, reads the result and decides the next step until the goal is met. For example, it might query a database, run Python on the result and write a summary. Agents handle multi-step tasks but are less predictable, slower and costlier than a single call. Good designs limit tools and steps, log every action and keep a human check for risky actions.
36
What is effect size and why should you report it along with a p-value?
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.
37
Pandas: compute a 7-day rolling average of daily sales for each store.
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.
Tips to crack the Zensar Technologies Data Science interview
Practise aptitude and basic programming questions for the online test, along with simple SQL queries.
Read about the RPG Group and Zensar's focus on digital experience and engineering services.
Prepare to explain your projects clearly, including tools used and testing done, in a short structured answer.
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; Zensar Technologies interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Zensar Technologies 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 Zensar Technologies Data Science interview?
This practice set leans on Machine Learning (8), Statistics and Probability (6), Python for Data Science (5), Deep Learning and NLP (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Zensar Technologies interviewers ask you to walk through real work.
Are these the exact questions asked at Zensar Technologies?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Zensar Technologies's. They are not a leaked or verified list of past questions.
Where does Zensar Technologies hire Data Science professionals in India?
Zensar Technologies has large teams in Pune, Hyderabad, Bengaluru. Openings and locations change often, so check the Zensar Technologies careers page and filter by city and experience.