01
What are RNNs and why did LSTMs improve them?
Show answer
RNNs process sequences with a hidden state but struggle with long dependencies. LSTMs add gates that control what to remember or forget, easing the vanishing gradient.
Data Science Interview Questions for 1 Year Experience
40 questions with answers for candidates with 1 year of experience — 16 easy, 23 medium, 1 hard.
What interviewers expect: 1 year experience
With one year of experience, interviewers expect you to have worked on real data and at least contributed to a model or analysis used by the business. They test practical SQL and Pandas, correct validation, handling messy data and imbalance, and how you communicated results. Questions shift from definitions to how you actually did things.
This set is weighted towards Machine Learning (9), Python for Data Science (5), Statistics and Probability (5), Deep Learning and NLP (4).
Real project data issues Window functions in SQL Model evaluation choices Communicating results
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02
What is the difference between a set and a dictionary?
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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.
03
What is stratified sampling in a train-test split?
Show answer
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.
04
What is the difference between hard voting and soft voting in ensembles?
Show answer
In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.
05
Annotators labelling your images often disagree with each other. How do you handle this?
Show answer
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.
06
What are polynomial features and what is the risk of using them?
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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.
07
What is a t-test and when do you use a paired t-test?
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A t-test compares means when the population variance is unknown. A two-sample t-test compares two independent groups, such as users in control and treatment. A paired t-test is used when the same units are measured twice, such as the same students before and after training. Pairing removes variation between individuals, so it is more powerful. Welch's t-test is preferred when the two groups have unequal variances.
08
Write Python code to compare revenue per user between two groups using Welch's t-test.
Show answer
import numpy as np
from scipy import stats
ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy()
treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy()
t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch
diff = treat.mean() - ctrl.mean()
se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl))
print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')
09
Your RAG chatbot often answers using the wrong documents. How do you fix it?
Show answer
First check retrieval on its own with a labelled set of questions, since generation cannot fix bad context. Then improve chunking so passages are self-contained, add metadata filters such as product or date, try a better embedding model, and add hybrid search with keywords. A reranker on the top 20 results often helps most. Also rewrite vague user questions into clearer search queries before retrieval.
10
Tell me about a time you found a mistake in your own analysis after sharing it.
Show answer
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.
11
What are guardrails in LLM applications?
Show answer
Guardrails are checks around the model that keep inputs and outputs safe and on-topic. Input guardrails detect prompt injection, abuse or off-topic requests. Output guardrails check for personal data, toxic content, policy violations or invalid formats before the answer reaches the user. They can be rules, classifiers or a second LLM call. Good guardrails also define fallback responses and log blocked cases for review.
12
What is a confusion matrix?
Show answer
A table of true positives, false positives, true negatives and false negatives from which accuracy, precision, recall and F1 are calculated.
13
What is the difference between WHERE and HAVING in SQL?
Show answer
WHERE filters individual rows before grouping, so it cannot use aggregate functions. HAVING filters groups after GROUP BY and can use aggregates. Example: WHERE order_date >= '2026-01-01' keeps only this year's orders, then GROUP BY customer_id HAVING SUM(amount) > 10000 keeps customers who spent more than 10,000. Filtering early with WHERE is also faster because fewer rows are grouped.
14
What are precision and recall trade-offs in practice?
Show answer
Raising the threshold improves precision but lowers recall. Fraud detection leans to recall, while spam filters often prefer precision; choose by the cost of each error.
15
What is named entity recognition?
Show answer
Named entity recognition finds and labels spans of text such as person names, organisations, locations, dates and amounts. For example, in an invoice email it can pull out the vendor, invoice number and total. It is usually framed as token classification with BIO tags. spaCy and fine-tuned transformer models are common tools. Domain-specific entities, like medicine names, usually need custom labelled training data.
16
What is U-Net and why is it popular for segmentation?
Show answer
U-Net is an encoder-decoder network. The encoder downsamples to capture context, and the decoder upsamples back to full resolution to produce a per-pixel mask. Skip connections copy encoder feature maps to the matching decoder level, so fine spatial detail lost during downsampling is recovered. It works well with limited labelled data, which made it the standard for medical imaging and industrial defect segmentation, and it is also the backbone of many diffusion models.
17
What is the difference between a function and a method?
Show answer
A method is a function that belongs to an object or class and receives the instance (self) as its first argument.
18
What are context managers in Python?
Show answer
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.
19
Explain p-value in simple words.
Show answer
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.
20
Explain exponential smoothing and the Holt-Winters method.
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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.
21
What is the difference between supervised and unsupervised learning?
Show answer
Supervised learning trains on labelled data to predict a target; unsupervised learning finds structure such as clusters or components in unlabelled data.
22
How do you handle imbalanced classes?
Show answer
Use stratified splits, class weights, resampling such as SMOTE, threshold tuning and judge models with precision, recall, F1 or PR-AUC instead of accuracy.
23
What is gradient descent and what does the learning rate do?
Show answer
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.
24
Write Pandas code to create useful features from a datetime column.
Show answer
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
25
What is the difference between a subquery and a correlated subquery?
Show answer
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.
26
What is bootstrapping in statistics?
Show answer
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.
27
Write a basic PyTorch training loop for one epoch.
Show answer
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
optimizer.zero_grad()
logits = model(X)
loss = loss_fn(logits, y)
loss.backward()
optimizer.step()
# evaluation
model.eval()
with torch.no_grad():
val_loss = sum(loss_fn(model(Xv.to(device)), yv.to(device)).item() for Xv, yv in val_loader)
28
What is the minimum detectable effect (MDE) in experiment design?
Show answer
The MDE is the smallest true effect your test can reliably detect given the sample size, baseline rate, significance level and power. If baseline conversion is 5 percent and your MDE is 10 percent relative, you can detect a move to 5.5 percent but will likely miss smaller lifts. Choose the MDE from business value: the smallest change worth shipping. A smaller MDE needs a much larger sample, roughly growing with one over MDE squared.
29
Tell me about a time you created documentation that helped your team work with data.
Show answer
Situation: new analysts kept misusing two similar revenue columns. Task: reduce repeated questions and errors. Action: I wrote a short data dictionary for our main tables, with definitions, known pitfalls and sample queries, and linked it in the team wiki and onboarding checklist. I updated it whenever schemas changed. Result: onboarding time dropped, and the number of revenue mismatches in reports fell noticeably within a quarter.
30
What is ANOVA and when would you use it?
Show answer
ANOVA, analysis of variance, tests whether three or more group means are equal. It compares variation between groups to variation within groups using an F statistic. For example, comparing average order value across four city tiers. A significant result says at least one mean differs but not which one, so you follow up with post-hoc tests like Tukey's HSD. It assumes independent samples, roughly normal residuals and similar variances.
31
How does linear regression work and what are its assumptions?
Show answer
It fits a line minimising squared error. Assumes linearity, independent errors, constant variance (homoscedasticity), normally distributed residuals and no strong multicollinearity.
32
What is the vanishing gradient problem?
Show answer
Gradients shrink as they pass through many layers (for example with sigmoid), so early layers barely learn. ReLU, residual connections and good initialisation help.
33
Your model's most important feature is customer_id. What is wrong?
Show answer
An ID has no real meaning, so high importance means the model is memorising individual customers. It often happens when the same customers appear in both train and test, or when IDs were assigned in time order and act as a proxy for date or signup cohort. I would drop the ID, use a group-based split by customer, and if timing matters, add an explicit feature like account age instead.
34
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.
35
How do you work with domain experts to find good features?
Show answer
Situation: on an insurance claims model, my first features gave average results. Task: improve fraud detection. Action: I sat with two claim investigators for a day and asked what made them suspicious. They mentioned claims filed soon after policy start and repeated garage names. I turned these into features and checked them with the team. Result: recall at the same precision rose by 12 points. Experts often know signals the data hides.
36
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.
37
What is PCA and when do you use it?
Show answer
It projects data onto orthogonal directions of maximum variance to reduce dimensions, remove correlation and speed up models, at some cost of interpretability.
38
Write Python code to group a list of order dictionaries by city and sum the amounts without Pandas.
Show answer
from collections import defaultdict
orders = [
{'city': 'Pune', 'amount': 250},
{'city': 'Delhi', 'amount': 400},
{'city': 'Pune', 'amount': 150},
]
totals = defaultdict(float)
for o in orders:
totals[o['city']] += o['amount']
print(dict(totals)) # {'Pune': 400.0, 'Delhi': 400.0}
39
How do you remove duplicates from a DataFrame?
Show answer
df.drop_duplicates(subset=[...], keep='first') removes repeated rows; use duplicated() first to inspect what will be dropped.
40
Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?
Show answer
This is intermittent demand, and standard models like ARIMA forecast a small constant that is useless for stocking. Croston's method and its SBA variant forecast the size of non-zero demand and the interval between sales separately. Alternatives are aggregating to weekly level, or using a global gradient boosting model with Tweedie or Poisson loss. Evaluate with MAE or MASE, not MAPE, because zero actuals break percentage errors.
Frequently asked questions
What is asked in Data Science interviews for 1 year of experience?
With one year of experience, interviewers expect you to have worked on real data and at least contributed to a model or analysis used by the business. They test practical SQL and Pandas, correct validation, handling messy data and imbalance, and how you communicated results. Questions shift from definitions to how you actually did things.
How many questions should I practise before a Data Science interview?
Work through all 40 questions here, then the company-wise sets for the employers you are targeting. Quality matters more than count: be able to explain why each answer is right.
Which topics matter most for 1 year of experience?
Focus first on Real project data issues, Window functions in SQL, Model evaluation choices, Communicating results.
Other experience levels
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