Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Zoho hiring for Data Science roles
Zoho is an Indian software company offering a suite of business applications including Zoho CRM, Zoho Books, Zoho Mail and Zoho Creator. It is privately held and builds most of its technology in-house. It hires for software developers, QA engineers and technical support roles, with a selection process known for programming-heavy rounds.
For Data Science roles, this Zoho practice set focuses most on Statistics and Probability (5), Machine Learning (5), MLOps and Model Deployment (5), Generative AI and LLMs (4). It has 2 easy, 28 medium, 10 hard questions, so you can start easy and work up to the harder ones.
Company type
Product
Headquarters
Chennai, India
Main India offices
Chennai, Tenkasi
Zoho Data Science interview process
Written test (aptitude + C programming output)
Basic programming round
Advanced programming round
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 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.
02
Write Pandas code to create useful features from a datetime column.
RFM stands for recency, frequency and monetary value. From the transactions table, for each customer as of a cut-off date: recency is days since the last purchase, frequency is the number of orders in the last 90 days, and monetary is total or average spend in that window. Compute them using only data before the cut-off, add trends like spend this month versus last, and the label from the period after.
04
Your LLM assistant confidently quotes a policy clause that does not exist. Why does this happen and how do you reduce it?
Hallucination is when a model produces confident text that is false or not supported by any source. It happens because the model generates plausible tokens rather than checking facts. Reduce it by grounding answers in retrieved documents, asking the model to cite sources and say I do not know when context is missing, using low temperature, and validating outputs with rules or a second checking step. Measure it with a labelled evaluation set.
05
Pandas: find customers whose monthly spend increased for three consecutive months.
In backtesting you used the actual temperature for future days, which you will never know at forecast time. In production the model gets a weather forecast, which has its own error, so performance drops. This is a subtle form of leakage. Fix the backtest by using the weather forecast that was available on each forecast date, or lagged actuals, so offline results match what production can really achieve.
Bootstrapping estimates the uncertainty of a statistic by resampling your data with replacement many times, computing the statistic on each resample, and looking at the spread of results. It gives confidence intervals for things with no simple formula, like the median, a ratio or a model metric such as AUC. It needs no normality assumption, but it relies on the sample being representative and can be slow on very large data.
08
What is the difference between parametric and non-parametric tests?
First measure agreement, for example with Cohen's kappa or IoU between annotators' boxes, to find which classes are confusing. Usually the guidelines are unclear, so write precise rules with example images for edge cases and retrain annotators. Have multiple people label a subset and resolve conflicts by majority vote or an expert reviewer. Track ambiguous images separately, since even a perfect model cannot beat human label noise.
10
What is DBSCAN and when is it better than K-means?
DBSCAN is a density-based clustering algorithm. It groups points that have at least min_samples neighbours within distance eps, and marks isolated points as noise. Unlike K-means, it does not need the number of clusters, finds clusters of any shape, and handles outliers. It struggles when clusters have very different densities and in high dimensions. It suits location data, like finding delivery hotspots from GPS points.
11
Write Python code to compare revenue per user between two groups using Welch's t-test.
Without randomisation I would use quasi-experimental methods. Difference-in-differences compares the change in a group that got the feature early with one that did not, if rollout was staggered. Interrupted time series or synthetic control builds a counterfactual from pre-launch trends or similar markets. Regression discontinuity works if eligibility had a cut-off. I would state the assumptions clearly and check them with placebo tests.
13
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.
14
Your LLM feature costs far more than planned. How do you reduce cost without hurting quality?
Measure cost per request and find the biggest token users. Shorten system prompts and retrieved context, and cap output length. Route simple requests to a smaller, cheaper model and keep the large model for hard ones. Cache repeated answers and use provider prompt caching for fixed prefixes. Batch offline jobs. Consider fine-tuning a small model for a high-volume narrow task. Check every change against the evaluation set.
15
An analyst tested 20 metrics in one experiment and found one significant. Should you trust it, and how do you correct for this?
Each test at 5 percent significance has a 5 percent false positive chance. Run 20 independent tests and you expect about one false positive even if nothing is real. Corrections include Bonferroni, which divides alpha by the number of tests and is strict, and Benjamini-Hochberg, which controls the false discovery rate and keeps more power. Also reduce the problem by choosing one primary metric before the experiment starts.
16
Halfway through a test you discover a bug that broke the treatment for Android users. What do you do?
Stop or pause the affected part, fix the bug and restart the test with fresh data, since mixing broken and fixed periods biases results. Do not simply drop Android users from analysis afterwards, because that can break randomisation balance if the bug affected behaviour or logging. Document the incident, and add pre-launch checks such as running the variant through QA on every platform and monitoring guardrails in the first hours.
17
Your manager asks whether collecting more training data will improve the model. How do you answer using a learning curve?
A learning curve plots training and validation scores against the size of the training set. If both scores are low and close, the model has high bias and needs more features or a more flexible model. If training is high but validation is much lower and the gap narrows as data grows, the model has high variance and more data will help. It tells you whether collecting more data is worth the cost.
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;
20
What is mean Average Precision (mAP) in object detection?
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.
21
A new checkout converts better on both mobile and desktop, but worse overall. How is that possible?
Simpson's paradox happens when a trend in several groups reverses when the groups are combined. Example: a new checkout converts better on both mobile and desktop, but worse overall, because most of its traffic came from mobile, which has lower conversion anyway. The mix of groups, not the treatment, drives the total. The lesson is to check segment-level results and confounding variables before drawing conclusions from aggregated numbers.
22
Pandas: compute a 7-day rolling average of daily sales for each store.
Power transforms reshape a skewed feature to look more normal. Box-Cox needs strictly positive values, while Yeo-Johnson also handles zero and negative values; both learn a lambda parameter from training data. A quantile transform maps values to a uniform or normal distribution by rank, which is robust to outliers. They help linear models, distance-based methods and neural networks, but tree models rarely need them.
24
For a warehouse, a stockout costs five times more than holding extra stock. How should the forecast change?
A forecast of the mean is the wrong target when costs are asymmetric. Forecast a quantile instead. The optimal quantile here is about 5 divided by 5 plus 1, roughly the 83rd percentile, which is the newsvendor critical ratio. Train with quantile or pinball loss, or use prediction intervals from a probabilistic model. Evaluate with pinball loss and the actual stockout and holding cost, not just MAE.
It combines gradient boosting with regularisation, handling of missing values, parallel tree construction and speed optimisations, and it performs strongly on tabular data.
26
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.
27
Tell me about a time you deployed a model under a tight deadline.
Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.
28
Write a Python function that deterministically assigns users to experiment variants using hashing.
import hashlib
def assign(user_id, experiment, variants=('control', 'treatment'), weights=(50, 50)):
key = f'{experiment}:{user_id}'.encode()
bucket = int(hashlib.sha256(key).hexdigest(), 16) % 100
cumulative = 0
for v, w in zip(variants, weights):
cumulative += w
if bucket < cumulative:
return v
# Same user + experiment always gets the same variant;
# salting with the experiment name keeps tests independent
29
Your fraud model's true labels arrive 30 days late. How do you monitor it in production?
Since accuracy cannot be measured quickly, monitor proxies. Track input data drift with PSI per feature, prediction score distribution, the share of transactions flagged, and data quality like missing values. Watch early signals such as chargeback complaints or manual review outcomes on a sample. Set alerts on sudden shifts. When labels arrive, compute real precision and recall by cohort and compare them with the proxy alerts to tune them.
30
Your deployed model gives different predictions than the same model in your notebook. Why?
Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.
31
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
The warning appears when you assign values to something that may be a copy of a slice instead of the original DataFrame, for example df[df.age > 30]['flag'] = 1. The original may not change. Fix it by using a single .loc call, like df.loc[df.age > 30, 'flag'] = 1, or by taking an explicit .copy() when you really want a separate DataFrame. Copy-on-write in newer Pandas versions makes this behaviour more predictable.
32
You added an index but the query is still slow. How do indexes work, and when do they fail to help or even hurt?
An index is a separate sorted structure, usually a B-tree, that lets the database find rows without scanning the whole table, like a book index. It speeds up filters, joins and sorting on the indexed columns. Indexes cost storage and slow down inserts, updates and deletes because each index must be updated. They may be ignored for low-selectivity columns or when a function is applied to the column in WHERE.
33
Write Pandas code to compute each customer's average order value excluding the current order.
g = df.groupby('customer_id')['amount']
total = g.transform('sum')
count = g.transform('count')
# Leave-one-out mean; NaN when it is the customer's only order
df['avg_other_orders'] = (total - df['amount']) / (count - 1)
df.loc[count == 1, 'avg_other_orders'] = float('nan')
# For time-aware features, use only earlier orders instead
34
Your random forest is accurate but too slow at prediction time for a real-time API. What do you do?
First measure where the time goes. Then reduce trees and maximum depth and check the accuracy loss. Switch to a gradient boosting model, which often needs fewer, smaller trees. Compile the model with tools like ONNX Runtime or Treelite. Cache features or precompute scores for known users. If accuracy allows, a simpler logistic regression may meet the latency budget easily. Always compare options on the same validation set.
Gradient boosting builds trees one after another. It starts with a simple prediction, like the mean. Each new small tree is fitted to the negative gradient of the loss, which for squared error is just the residuals. Its predictions are multiplied by a learning rate and added to the model. Repeating this slowly reduces error. Key settings are number of trees, learning rate, tree depth and subsampling, which together control overfitting.
36
Describe a production incident with a model and how you handled it.
Situation: one morning our recommendation model started showing the same five products to all users. Task: restore service and find the cause. Action: I rolled back to the previous model version within 20 minutes, then traced the issue to an upstream table that had arrived empty, so all user features were defaults. Result: we added data volume checks before scoring and a blameless postmortem. Rollback readiness saved us that day.
37
What are embeddings and vector databases, and why do LLM apps use them?
An embedding model turns text into a vector so that similar meanings are close in space. A vector database stores these vectors with metadata and finds the nearest ones to a query vector quickly using approximate nearest neighbour indexes like HNSW. LLM apps use them for semantic search and retrieval-augmented generation, finding the most relevant documents to put into the prompt. Examples include pgvector, FAISS and dedicated vector stores.
38
Your neural network's training loss suddenly becomes NaN. What do you check?
Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.
39
Your test shows a 2 percent lift in conversion, but revenue per user went down. What do you do?
Do not ship on conversion alone. Check whether the change shifted buyers to cheaper items, increased discount usage or pulled in low-value orders. Break down revenue into conversion times average order value, look at item mix, and confirm the revenue drop is statistically meaningful rather than noise. Then go back to the agreed primary metric and guardrails; if revenue is the business goal, it should guide the decision.
40
Write SQL to calculate month-over-month revenue growth.
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month, revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct
FROM monthly;
Tips to crack the Zoho Data Science interview
Practise C programming output and pointer questions, which commonly appear in Zoho's written test.
Prepare for advanced programming rounds where you build a small application, such as a booking system, without external libraries.
Learn about Zoho's privacy-focused, in-house product approach and its rural offices in places like Tenkasi.
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; Zoho interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Zoho Data Science interview?
Candidates usually go through 5 stages: Written test (aptitude + C programming output); Basic programming round; Advanced programming round; 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 Zoho Data Science interview?
This practice set leans on Statistics and Probability (5), Machine Learning (5), MLOps and Model Deployment (5), Generative AI and LLMs (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Zoho interviewers ask you to walk through real work.
Are these the exact questions asked at Zoho?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Zoho's. They are not a leaked or verified list of past questions.
Where does Zoho hire Data Science professionals in India?
Zoho has large teams in Chennai, Tenkasi. Openings and locations change often, so check the Zoho careers page and filter by city and experience.