01
How do you use groupby with multiple aggregations?
Show answer
df.groupby('region').agg(total=('sales','sum'), avg=('sales','mean')) gives named aggregations per group in one pass.
LatentView Analytics Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About LatentView Analytics hiring for Data Science roles
LatentView Analytics is an Indian data analytics company providing data engineering, business analytics and data science services to global clients, many in technology, consumer goods and financial services. It hires in India for analysts, data engineers and data scientists who work on dashboards, data pipelines and predictive models.
For Data Science roles, this LatentView Analytics practice set focuses most on SQL for Data Science (8), Deep Learning and NLP (7), Python for Data Science (6), MLOps and Model Deployment (4). It has 9 easy, 26 medium, 5 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Analytics
- Headquarters
- Chennai, India
- Main India offices
- Chennai, Bengaluru
LatentView Analytics Data Science interview process
- Online assessment (aptitude + SQL)
- Technical interview
- Case or guesstimate round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
Why is cross-entropy preferred over MSE for classification in neural networks?
Show answer
Cross-entropy measures how far predicted probabilities are from the true class and strongly penalises confident wrong predictions. With a softmax or sigmoid output, it gives large, useful gradients when the model is wrong. MSE with sigmoid outputs gives very small gradients when predictions saturate, so learning slows down. Use binary cross-entropy for two classes, categorical cross-entropy for many classes, and MSE for regression targets.
03
What is a time series and how is it different from ordinary tabular data?
Show answer
A time series is a sequence of observations recorded at ordered time steps, such as daily sales or hourly website traffic. Unlike ordinary tabular data, rows are not independent: today's value depends on yesterday's, and the order cannot be shuffled. This changes everything downstream. You must split train and test by time, build features only from the past, and check for trend and seasonality before choosing a model.
04
What is batch normalisation?
Show answer
It normalises layer inputs per mini-batch and then rescales them, stabilising and speeding up training and allowing higher learning rates.
05
How do list comprehensions work and why use them?
Show answer
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.
06
Write SQL to compute a 7-day rolling average of daily active users.
Show answer
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;
07
What are Python's main built-in data types?
Show answer
int, float, complex, bool, str, list, tuple, set, dict and NoneType. Mutable ones are list, set and dict; the others are immutable.
08
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}')
09
Your LLM gives different summaries for the same document on different runs, but auditors need consistent output. What do you do?
Show answer
Set temperature to zero or near zero and fix other sampling settings, and use a seed where the API supports it, though outputs may still vary slightly. Pin the exact model version. Use a structured output format with fixed fields, and reduce ambiguity in the prompt. Cache and store each generated output with its inputs and version, so the stored result, not a regeneration, is what auditors see.
10
Your fraud model's true labels arrive 30 days late. How do you monitor it in production?
Show answer
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.
11
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.
12
Tell me about a time you had to deliver data under an urgent deadline.
Show answer
Situation: the leadership team needed festive-sale numbers by city for a 9 am review, and the dashboard pipeline had failed overnight. Task: deliver accurate numbers in three hours. Action: I queried the raw orders tables directly, reused validated logic from the dashboard SQL, cross-checked totals with finance's daily figure and flagged two cities with partial data. Result: the review went ahead on time, and I later added pipeline failure alerts.
13
What is the difference between batch and real-time inference?
Show answer
Batch inference scores many records on a schedule, for example nightly churn scores for all customers written to a table. It is cheap and simple. Real-time inference serves predictions on request through an API within milliseconds, for example fraud checks during payment. It needs low-latency feature access, scaling and high availability. Choose real-time only when the decision truly must use fresh data at the moment of the request.
14
Your neural network's training loss suddenly becomes NaN. What do you check?
Show answer
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.
15
What is the logical order of execution of a SQL query?
Show answer
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.
16
You need to extract fields from scanned invoices that come in hundreds of different layouts. How would you design it?
Show answer
Fixed templates will not scale to hundreds of layouts. Start with preprocessing: deskew, denoise and detect text regions. Run OCR to get words with their positions. Then use a layout-aware document model, or a vision-language model, to map words to fields like invoice number, GSTIN and total. Add validation rules, such as GSTIN format and line items summing to the total, and send low-confidence documents to human review.
17
After joining orders with a promotions table, total revenue doubled. What went wrong?
Show answer
The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.
18
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.
19
Write Pandas code to read a large CSV in chunks and compute total sales per city.
Show answer
# process 1 million rows at a time
import pandas as pd
totals = {}
for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000):
part = chunk.groupby('city')['amount'].sum()
for city, amt in part.items():
totals[city] = totals.get(city, 0) + amt
result = pd.Series(totals).sort_values(ascending=False)
20
L1 vs L2 regularisation?
Show answer
L1 (Lasso) adds absolute weights and can shrink some to zero, giving feature selection. L2 (Ridge) adds squared weights and shrinks them smoothly without removing features.
21
Difference between a shallow copy and a deep copy?
Show answer
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.
22
Your GPU model-serving bill has doubled while traffic grew only 20 percent. How do you bring cost down?
Show answer
Profile utilisation first; idle or under-filled GPUs are common. Enable dynamic batching so each GPU call handles several requests, right-size instance types and autoscale on queue depth rather than CPU. Reduce model cost with quantisation, distillation or a smaller model for easy requests. Cache repeated predictions, move non-urgent work to batch jobs on cheaper hardware, and track cost per thousand predictions as a standing metric.
23
Your product image classifier works on studio catalogue photos but fails on photos users take with their phones. Why, and what do you do?
Show answer
This is domain shift: user photos have clutter, blur, poor lighting and odd angles that the model never saw. Collect and label a sample of real user photos and evaluate on them separately. Add augmentation for blur, noise, colour jitter, rotation and random crops, and fine-tune on a mix of studio and real images. Monitor accuracy on user photos in production, since that is the distribution that matters.
24
How does a CNN work?
Show answer
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
25
You assign values to a filtered DataFrame and Pandas shows a SettingWithCopyWarning. What is going on and how do you fix it?
Show answer
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.
26
Training a transformer on a GPU fails with an out-of-memory error. What can you do?
Show answer
Reduce the batch size and use gradient accumulation to keep the same effective batch. Shorten the maximum sequence length. Turn on mixed precision training with float16 or bfloat16. Use gradient checkpointing, which saves memory by recomputing activations. Freeze some layers or switch to a smaller model, or use parameter-efficient fine-tuning like LoRA. Also make sure no old tensors stay on the GPU between runs.
27
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.
28
Explain Bayes' theorem with an example.
Show answer
P(A given B) = P(B given A) x P(A) / P(B). In a medical test it converts test accuracy and disease prevalence into the real chance a positive result means disease.
29
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;
30
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.
31
Write Python code to fit a Holt-Winters model and forecast the next 12 months.
Show answer
from statsmodels.tsa.holtwinters import ExponentialSmoothing
s = df.set_index('month')['sales'].asfreq('MS')
train, test = s[:-12], s[-12:]
model = ExponentialSmoothing(
train, trend='add', seasonal='mul',
seasonal_periods=12
).fit()
forecast = model.forecast(12)
mae = (forecast - test).abs().mean()
print(forecast.round(1), 'MAE:', round(mae, 1))
32
Your model API meets average latency but p99 latency is 900 ms against a 200 ms target. What do you do?
Show answer
Profile slow requests to see if time goes to feature fetching, preprocessing, the model or the network. Common fixes: precompute and cache features, remove slow database calls from the request path, load the model once at start-up, use a faster runtime like ONNX, and warm up instances. Check garbage collection pauses, cold starts and autoscaling limits. Set timeouts with a safe fallback prediction for rare slow cases.
33
What is SHAP and how does it explain model predictions?
Show answer
SHAP assigns each feature a contribution to a single prediction based on Shapley values from game theory. The contributions add up to the difference between the prediction and the average prediction. It gives local explanations, like why one loan was rejected, and global views by aggregating values across rows. TreeSHAP is fast for tree models. Limits include slow computation for other models and misleading results when features are strongly correlated.
34
A deep network you built from scratch does not learn at all; the loss stays flat from the first epoch. Could weight initialisation be the reason?
Show answer
If weights start too large, activations and gradients explode; if too small, they vanish, and training stalls. If all weights are equal, every neuron learns the same thing. Good schemes keep the variance of activations stable across layers: Xavier or Glorot initialisation for tanh and sigmoid, and He initialisation for ReLU. Frameworks use sensible defaults, but it matters when building custom layers or very deep networks.
35
Write Python code to find the top 3 most similar documents to a query using cosine similarity on embeddings.
Show answer
import numpy as np
def top_k(query_vec, doc_vecs, k=3):
q = query_vec / np.linalg.norm(query_vec)
d = doc_vecs / np.linalg.norm(doc_vecs, axis=1, keepdims=True)
scores = d @ q
idx = np.argsort(-scores)[:k]
return list(zip(idx, scores[idx]))
# doc_vecs shape: (n_docs, dim), query_vec shape: (dim,)
36
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.
37
Write SQL to find customers who ordered in January 2026 but not in February 2026.
Show answer
SELECT DISTINCT o.customer_id
FROM orders o
WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01'
AND NOT EXISTS (
SELECT 1 FROM orders f
WHERE f.customer_id = o.customer_id
AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01'
);
38
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.
39
Your NLP classifier works well on English reviews but fails on Hinglish reviews. What do you do?
Show answer
First measure the gap with a labelled Hinglish test set. Hinglish mixes scripts and spellings, so tokenisers built for English split it badly. I would try a multilingual or Indic model such as mBERT, XLM-R or IndicBERT, normalise common spellings, and add labelled Hinglish data, possibly with transliteration. Evaluate per language segment and monitor the language mix in production, because it changes over time.
40
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.
Tips to crack the LatentView Analytics Data Science interview
- Practise SQL queries and Excel analysis, as many LatentView roles start with reporting and data preparation.
- Prepare guesstimates and simple business cases, explaining your assumptions and calculation steps clearly to the interviewer.
- Be ready to explain one dashboard or analysis project end to end, from data cleaning to insight.
- 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; LatentView Analytics interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the LatentView Analytics Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + SQL); Technical interview; Case or guesstimate round; 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 LatentView Analytics Data Science interview?
This practice set leans on SQL for Data Science (8), Deep Learning and NLP (7), Python for Data Science (6), MLOps and Model Deployment (4). Revise those first, then practise explaining one or two of your own projects end to end, because most LatentView Analytics interviewers ask you to walk through real work.
Are these the exact questions asked at LatentView Analytics?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like LatentView Analytics's. They are not a leaked or verified list of past questions.
Where does LatentView Analytics hire Data Science professionals in India?
LatentView Analytics has large teams in Chennai, Bengaluru. Openings and locations change often, so check the LatentView Analytics careers page and filter by city and experience.
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