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Data Science Telephonic Interview Questions & Answers

30 questions for the telephonic interview stage — 18 easy, 12 medium.

About the telephonic interview round

The telephonic or screening round is a 15-30 minute call with a recruiter or engineer. It checks your basics, your CV and whether you can explain Data Science concepts clearly without a screen to point at.

Tips for the telephonic interview round

  • Keep your CV in front of you and be ready to explain every line on it.
  • Answer in short, clear sentences; there is no whiteboard, so structure matters.
  • Take the call somewhere quiet and confirm the time in advance.
  • Prepare one question to ask the interviewer at the end.
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01

What is a model registry and why do teams use one?

MLOps and Model DeploymentEasy
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A model registry is a central store for trained model versions along with their metadata: training data version, code commit, parameters, metrics and owner. Models move through stages such as staging, production and archived, with approvals. It gives one source of truth for which model is live, makes rollback to a previous version easy and supports audits. Tools like MLflow Model Registry are commonly used.
02

What is the difference between a base model and an instruction-tuned model?

Generative AI and LLMsEasy
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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.
03

You keep adding features and R-squared keeps rising. Is the model really getting better?

Machine LearningEasy
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R-squared shows the share of variance in the target explained by the model. It never decreases when you add a feature, even a useless one. Adjusted R-squared penalises extra features based on the number of predictors and sample size, so it rises only if a new feature improves the model more than chance would. Use adjusted R-squared to compare linear models with different numbers of features.
04

What is the difference between mean, median and mode?

Statistics and ProbabilityEasy
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Mean is the average, median is the middle value after sorting and mode is the most frequent value. Median is more robust to outliers.
05

How do you make a machine learning experiment reproducible?

MLOps and Model DeploymentMedium
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Version the code with Git, the data with a tool like DVC or dated snapshots, and the environment with pinned requirements or Docker. Set random seeds for every library. Log parameters, metrics and the exact data version for each run with a tracker like MLflow. Keep preprocessing inside a pipeline rather than manual notebook steps. Then anyone should be able to rebuild the same model from a commit and a data version.
06

What is NumPy broadcasting?

Python for Data ScienceEasy
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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.
07

What are Python's main built-in data types?

Python for Data ScienceEasy
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int, float, complex, bool, str, list, tuple, set, dict and NoneType. Mutable ones are list, set and dict; the others are immutable.
08

What do temperature and top-p control in LLM generation?

Generative AI and LLMsEasy
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Both control randomness when the model picks the next token. Temperature scales the probabilities: low values like 0 to 0.3 make output focused and repeatable, high values make it more varied and creative. Top-p, nucleus sampling, keeps only the smallest set of tokens whose probabilities add up to p, such as 0.9, and samples from them. For extraction and classification use low temperature; for brainstorming use higher values.
09

How do you handle rare categories in a categorical feature?

Feature EngineeringEasy
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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.
10

What is stationarity and why do many forecasting models need it?

Time Series & ForecastingEasy
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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.
11

What do ACF and PACF plots tell you about a time series?

Time Series & ForecastingMedium
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The ACF plot shows correlation between the series and its own lags, including indirect effects through intermediate lags. The PACF shows the correlation at each lag after removing the effect of shorter lags. For a stationary series, a PACF that cuts off after lag p suggests an AR(p) term, and an ACF that cuts off after lag q suggests an MA(q) term. Spikes at lag 7 or 12 reveal weekly or yearly seasonality.
12

What is standard error and how is it different from standard deviation?

Statistics and ProbabilityEasy
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Standard deviation measures how spread out individual data points are. Standard error measures how much a sample statistic, usually the mean, would vary if you repeated the sampling. For the mean, SE = SD divided by the square root of n. So with more data the standard deviation stays roughly the same, but the standard error shrinks. Confidence intervals and t-tests are built on the standard error.
13

Tell me about a time you improved messy code written by someone else.

Python for Data ScienceEasy
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Situation: in my final-year project, a teammate's cleaning script was one 600-line notebook that took 20 minutes to run. Task: I needed to reuse it for new data. Action: I split it into small functions, added docstrings, replaced row loops with vectorised Pandas code and wrote a few checks for row counts. Result: run time fell to 2 minutes and the team could reuse the functions. I learned to refactor in small, tested steps.
14

What is early stopping?

Machine LearningEasy
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Early stopping monitors a validation metric during training and stops when it has not improved for a set number of rounds, called patience. It is used in gradient boosting and neural networks to prevent overfitting and save time. The model from the best round is kept. The validation set must not be the final test set, otherwise the reported test score becomes optimistic.
15

What are window functions in SQL?

SQL for Data ScienceEasy
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Window functions compute a value for each row using a set of related rows, without collapsing them like GROUP BY does. They use OVER with optional PARTITION BY and ORDER BY. Examples are ROW_NUMBER for ranking, SUM OVER for running totals, LAG and LEAD for previous and next values, and AVG over a frame for moving averages. They are central to data science SQL interviews.
16

What are dataclasses in Python and why are they useful in data projects?

Python for Data ScienceEasy
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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.
17

Difference between a shallow copy and a deep copy?

Python for Data ScienceMedium
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A shallow copy duplicates the outer object but shares nested objects. A deep copy (copy.deepcopy) recursively duplicates everything, so changes to nested items do not leak.
18

Write Pandas code to create useful features from a datetime column.

Feature EngineeringEasy
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df['ts'] = pd.to_datetime(df['ts']) df['hour'] = df['ts'].dt.hour df['dayofweek'] = df['ts'].dt.dayofweek df['is_weekend'] = df['dayofweek'].isin([5, 6]).astype(int) df['month'] = df['ts'].dt.month df['days_since_signup'] = (df['ts'] - df['signup_date']).dt.days
19

Pearson vs Spearman correlation?

Statistics and ProbabilityEasy
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Pearson measures linear relationships on continuous data; Spearman uses ranks, so it captures monotonic relationships and is less sensitive to outliers.
20

What is sampling bias and how can you reduce it?

Statistics and ProbabilityMedium
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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.
21

How does the Gini impurity differ from entropy?

Machine LearningMedium
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Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.
22

Explain train, validation and test sets.

Machine LearningEasy
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Train fits the model, validation tunes hyperparameters and model choice, and the untouched test set gives a final unbiased estimate of performance.
23

Write SQL to calculate month-over-month revenue growth.

SQL for Data ScienceMedium
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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;
24

What is calibration of a classifier?

Machine LearningMedium
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Calibration checks whether predicted probabilities match real frequencies (e.g. 70% predictions are right about 70% of the time). Fix with Platt scaling or isotonic regression.
25

Why do residual connections make very deep networks easier to train?

Computer VisionMedium
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A residual block adds its input to its output, so the layers learn a residual F(x) and the block outputs F(x) plus x. If extra layers are not useful, they can learn to output near zero and the block behaves like identity. The skip path also lets gradients flow directly to early layers, reducing vanishing gradients. This is why ResNets with 50 or 100-plus layers train well where plain deep networks degrade.
26

How do you profile and speed up slow Python code?

Python for Data ScienceMedium
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Measure first with cProfile or timeit, then replace loops with NumPy or Pandas vectorised calls, use built-ins, caching and better data structures.
27

What is Naive Bayes and why is it called naive?

Machine LearningMedium
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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.
28

What is an Overall Evaluation Criterion (OEC) in experimentation?

A/B Testing & ExperimentationMedium
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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.
29

What is the difference between saving a model with pickle, joblib and ONNX?

MLOps and Model DeploymentMedium
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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.
30

What is the logical order of execution of a SQL query?

SQL for Data ScienceEasy
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Although you write SELECT first, the database logically processes FROM and JOIN first, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because WHERE runs before SELECT, but you can use it in ORDER BY. Window functions are computed after WHERE and GROUP BY, so filter on them in an outer query.

Frequently asked questions

What happens in the Data Science telephonic interview round?

The telephonic or screening round is a 15-30 minute call with a recruiter or engineer. It checks your basics, your CV and whether you can explain Data Science concepts clearly without a screen to point at.

How do I clear the telephonic interview round?

Keep your CV in front of you and be ready to explain every line on it. Answer in short, clear sentences; there is no whiteboard, so structure matters. Take the call somewhere quiet and confirm the time in advance.

Which round comes next?

A typical order is online assessment, telephonic screening, one or two technical rounds, a managerial round and HR. Use the other round pages to practise each stage.

Practise every interview round

Online Assessment questionsTechnical Round questionsManagerial Round questionsRapid-Fire questionsHR & behavioural questionsFull mock interview

More ways to practise

Company-wise Data Science questionsPackage-wise Data Science questionsExperience-wise Data Science questionsRole-wise Data Science questionsCity-wise Data Science questionsMost asked Data Science questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Science mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Science topic sheets