01
Why is XGBoost so popular?
Show answer
It combines gradient boosting with regularisation, handling of missing values, parallel tree construction and speed optimisations, and it performs strongly on tabular data.
Mphasis Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Mphasis hiring for Data Science roles
Mphasis is an Indian IT services company, majority-owned by Blackstone, providing cloud, application, testing, data and cognitive services. It has a strong presence in banking, financial services and insurance clients. In India it hires for software engineers, test engineers, data analysts and data engineers, often for BFSI-focused projects.
For Data Science roles, this Mphasis practice set focuses most on Machine Learning (7), Time Series & Forecasting (6), Statistics and Probability (5), Python for Data Science (4). It has 17 easy, 23 medium questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Bengaluru, India
- Main India offices
- Bengaluru, Pune, Chennai
Mphasis 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
Write SQL to find duplicate emails in a users table.
Show answer
SELECT email, COUNT(*) AS cnt
FROM users
GROUP BY email
HAVING COUNT(*) > 1
ORDER BY cnt DESC;
03
What is the difference between a set and a dictionary?
Show answer
A set stores unique values with no keys; a dictionary stores key-value pairs with unique keys. Both use hashing, so lookups are O(1) on average.
04
How do you handle rare categories in a categorical feature?
Show answer
Categories that appear only a few times give unreliable signals and create many sparse columns. A common fix is to group all categories below a frequency threshold, like 1 percent, into an Other bucket. You can also use frequency encoding or smoothed target encoding. Make sure the encoder handles unseen categories at prediction time, for example with handle_unknown set to ignore.
05
What is the difference between bagging and boosting?
Show answer
Bagging trains models in parallel on bootstrap samples and averages them (reduces variance, e.g. Random Forest). Boosting trains models one after another, each fixing the previous errors (reduces bias, e.g. XGBoost).
06
What is an Overall Evaluation Criterion (OEC) in experimentation?
Show answer
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.
07
What is differencing in time series analysis?
Show answer
Differencing replaces each value with the change from the previous value: y_t minus y_(t-1). It removes a linear trend and often makes a series stationary. Seasonal differencing subtracts the value one season back, such as y_t minus y_(t-7) for daily data with a weekly cycle. Avoid over-differencing, because it adds noise and negative autocorrelation. In ARIMA, the d parameter is the number of times you difference.
08
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.
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 the difference between saving a model with pickle, joblib and ONNX?
Show answer
pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.
11
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.
12
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.
13
Write Python code to decompose a monthly series into trend, seasonal and residual parts with STL.
Show answer
import pandas as pd
from statsmodels.tsa.seasonal import STL
s = df.set_index('month')['sales'].asfreq('MS')
res = STL(s, period=12, robust=True).fit()
parts = pd.DataFrame({
'trend': res.trend,
'seasonal': res.seasonal,
'resid': res.resid
})
print(parts.tail())
res.plot()
14
How do you detect outliers?
Show answer
Use the IQR rule (below Q1-1.5 IQR or above Q3+1.5 IQR), z-scores beyond 3, box plots or model-based methods such as Isolation Forest, then decide whether to cap, remove or keep them.
15
How does K-means clustering work and how do you choose k?
Show answer
It assigns points to the nearest centroid and updates centroids until stable. Choose k with the elbow method, silhouette score or business context.
16
What is an Airflow DAG?
Show answer
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.
17
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.
18
Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?
Show answer
The model is treating Diwali as noise because the festival moves on the calendar and a fixed yearly seasonality does not align. Add explicit holiday features: a flag for Diwali and a few days before and after, since buying starts early. Include other regional festivals and sale events too. Models like Prophet or gradient boosting with holiday features handle this well. Then backtest specifically on past festive periods to confirm the fix.
19
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
Show answer
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import brier_score_loss
base = HistGradientBoostingClassifier()
cal = CalibratedClassifierCV(base, method='isotonic', cv=5)
cal.fit(X_train, y_train)
prob = cal.predict_proba(X_test)[:, 1]
print('Brier:', brier_score_loss(y_test, prob))
20
How does the Gini impurity differ from entropy?
Show answer
Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.
21
How do you pull large data from a SQL database into Pandas efficiently?
Show answer
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
22
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.
23
What is DBSCAN and when is it better than K-means?
Show answer
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.
24
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.
25
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.
26
What is a confidence interval?
Show answer
A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.
27
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.
28
What is function calling or tool use in LLMs?
Show answer
Function calling lets an LLM respond with a structured request to call a tool you defined, such as get_order_status with an order_id, instead of plain text. Your code runs the function, returns the result to the model, and the model uses it in its final answer. This connects LLMs to live data, calculators and APIs. You must validate arguments, limit which tools exist and handle errors, because the model can choose wrong inputs.
29
Users say your internal chatbot gives outdated policy answers. What is the likely cause and fix?
Show answer
The knowledge index probably still holds old document versions, or the model is answering from its training data instead of retrieved context. Fix the ingestion pipeline to re-index on document change and remove superseded versions. Store effective dates in metadata and filter or boost the latest. Instruct the model to answer only from provided context and show the source with its date, so users can verify it.
30
Your experiment ended with no statistically significant difference. What do you tell the team?
Show answer
Not significant does not mean no effect. Report the observed effect with its confidence interval, for example minus 0.3 to plus 1.2 percent, and compare it with the MDE the test was powered for. If the interval rules out effects that matter, conclude the change has little impact and decide based on cost or design preference. If the interval is wide, the test was underpowered, and a longer test or variance reduction may help.
31
What is a normal distribution?
Show answer
A symmetric bell-shaped distribution defined by mean and standard deviation, where about 68%, 95% and 99.7% of values lie within 1, 2 and 3 standard deviations.
32
Write a Dockerfile for a Python model API.
Show answer
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app/ ./app
COPY model.joblib .
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
33
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.
34
Write Python code to evaluate a forecasting model with expanding-window time series cross-validation.
Show answer
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_absolute_error
tscv = TimeSeriesSplit(n_splits=5, test_size=28)
scores = []
for tr, te in tscv.split(X):
model.fit(X.iloc[tr], y.iloc[tr])
pred = model.predict(X.iloc[te])
scores.append(mean_absolute_error(y.iloc[te], pred))
print(np.mean(scores), scores)
35
Write SQL to find the top 3 products by revenue in each category.
Show answer
WITH rev AS (
SELECT category, product_id, SUM(price * qty) AS revenue
FROM order_items
GROUP BY category, product_id
), ranked AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY category ORDER BY revenue DESC) AS rn
FROM rev
)
SELECT category, product_id, revenue
FROM ranked
WHERE rn <= 3;
36
Write Python code for a two-proportion z-test comparing conversion between control and treatment.
Show answer
from statsmodels.stats.proportion import proportions_ztest, proportion_confint
conversions = [480, 540] # control, treatment
visitors = [10000, 10000]
z, p = proportions_ztest(conversions, visitors)
lift = conversions[1] / visitors[1] - conversions[0] / visitors[0]
ci_t = proportion_confint(conversions[1], visitors[1], method='wilson')
print(f'z={z:.2f} p={p:.4f} abs lift={lift:.4f} treatment CI={ci_t}')
37
What is a neural network?
Show answer
Layers of connected neurons that apply weights, biases and activation functions to learn complex patterns, trained by backpropagation.
38
What are tokens and the context window in an LLM?
Show answer
Tokens are the pieces of text an LLM reads and writes, often word parts; in English, one token is roughly three quarters of a word. The context window is the maximum number of tokens the model can consider at once, including your prompt, any documents and its answer. Costs and speed depend on token counts. Long documents beyond the window must be chunked, summarised or retrieved selectively.
39
When would you use MAE, MSE or RMSE for a regression model?
Show answer
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.
40
What should you log for a production prediction service?
Show answer
For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.
Tips to crack the Mphasis Data Science interview
- Learn BFSI basics such as banking transactions, insurance claims and mortgages, since many Mphasis clients are in this domain.
- Revise testing concepts, SQL and one programming language well for technical interviews.
- Read about Mphasis's Front2Back transformation approach, which the company uses to describe its service model.
- 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; Mphasis interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Mphasis 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 Mphasis Data Science interview?
This practice set leans on Machine Learning (7), Time Series & Forecasting (6), Statistics and Probability (5), Python for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Mphasis interviewers ask you to walk through real work.
Are these the exact questions asked at Mphasis?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Mphasis's. They are not a leaked or verified list of past questions.
Where does Mphasis hire Data Science professionals in India?
Mphasis has large teams in Bengaluru, Pune, Chennai. Openings and locations change often, so check the Mphasis careers page and filter by city and experience.
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