01
What is statistical power and how can you increase it?
Show answer
Power is the probability of detecting a real effect. Increase it with a larger sample, a bigger effect size, lower noise or a higher significance level.
Autodesk Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Autodesk hiring for Data Science roles
Autodesk is a software company known for design and engineering products such as AutoCAD, Revit, Fusion and Maya. Architects, engineers, manufacturers and media professionals use its tools for design, simulation and visual effects. In India it has development and operations centres that hire software engineers, quality engineers, data analysts and technical support roles.
For Data Science roles, this Autodesk practice set focuses most on Statistics and Probability (8), Time Series & Forecasting (5), A/B Testing & Experimentation (5), Python for Data Science (4). It has 2 easy, 25 medium, 13 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Product
- Headquarters
- San Francisco, USA
- Main India offices
- Bengaluru, Pune
Autodesk Data Science interview process
- Online coding assessment
- Technical round 1 (DSA)
- Technical round 2 (design or C++/domain)
- Hiring manager round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
Write Python code to run an ADF stationarity test and difference the series if needed.
Show answer
from statsmodels.tsa.stattools import adfuller
def is_stationary(s, alpha=0.05):
p_value = adfuller(s.dropna())[1]
return p_value < alpha
series = s.copy()
d = 0
while not is_stationary(series) and d < 2:
series = series.diff()
d += 1
print('differencing order d =', d)
03
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.
04
What is a sample ratio mismatch in an experiment and what do you do about it?
Show answer
A sample ratio mismatch means the observed split between groups differs from the planned split, for example 52 to 48 instead of 50 to 50 with a large sample. A chi-square test on the counts confirms it. It usually signals a bug in assignment, redirects, bot filtering or logging, so the results cannot be trusted. Do not analyse the metric; find and fix the root cause and rerun the test.
05
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())
)
06
Marketing wants to measure the impact of a TV and outdoor campaign that cannot be randomised at user level. How do you design the measurement?
Show answer
Plan a geo experiment: select comparable cities or regions, randomly assign some to run the campaign and keep others as control, matching them on past sales trends. Measure the difference in sales during and after the campaign, using methods like difference-in-differences or synthetic control to build a counterfactual. Run a pre-period check that test and control regions track each other, and include enough regions for reasonable power.
07
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Show answer
Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.
08
Write a Python function that deterministically assigns users to experiment variants using hashing.
Show answer
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
09
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.
10
A key metric dropped 8 percent week over week. How do you check whether it is real?
Show answer
First rule out data issues: tracking changes, pipeline delays, missing partitions or a changed metric definition. Then compare with the same week last year and look for holidays or seasonality. Check normal weekly variation to see if 8 percent is unusual. Break the drop down by platform, region, app version and user type to find where it is concentrated. Finally, check releases, campaigns or outages that happened that week.
11
Your LLM assistant confidently quotes a policy clause that does not exist. Why does this happen and how do you reduce it?
Show answer
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.
12
You must forecast demand for a new product that has only three weeks of history. What do you do?
Show answer
Three weeks is too little to learn seasonality, so borrow information. Find similar past launches by category, price band and channel, and use their early sales curves as analogues. A global model trained across many products, with product attributes as features, can also forecast a cold-start item. Combine this with planned marketing spend and business judgement, show a wide interval, and update the forecast weekly as real data arrives.
13
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.
14
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;
15
A product manager wants to test five different button designs at the same time. How do you set it up?
Show answer
Run an A/B/n test with control plus five variants and equal random allocation. Calculate sample size for the smallest effect worth detecting, remembering that each variant gets only a sixth of traffic, so the test takes longer. Plan the analysis upfront: compare each variant to control and adjust for multiple comparisons. If the team has strong priors, test fewer designs first to save time.
16
What is the difference between recursive and direct multi-step forecasting?
Show answer
In recursive forecasting you train a one-step model and feed each prediction back as an input to predict the next step, so errors compound over the horizon. In direct forecasting you train a separate model for each horizon, such as one for t+1 and another for t+7, which avoids compounding but costs more and may give inconsistent forecasts. Hybrid and multi-output approaches sit in between and are common in production.
17
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.
18
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))
19
After one-hot encoding you have 2,000 features and training is slow. What do you do?
Show answer
Find which columns caused the explosion, usually a few high-cardinality categoricals. Group rare categories into Other, or switch those columns to frequency, target or hashing encoding. Use sparse matrices so zeros take no memory. Tree libraries like LightGBM and CatBoost can handle categoricals natively without one-hot. Then remove near-constant columns and check that validation scores hold up with the smaller feature set.
20
How would you build a churn prediction model end to end?
Show answer
Define churn and the prediction window, build behavioural features, split by time, train and compare models, evaluate with recall and lift, explain drivers and monitor drift after deployment.
21
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
Show answer
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.
22
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.
23
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.
24
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.
25
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.
26
A feature works well offline but is not available at prediction time in production. How do you handle this?
Show answer
This is training-serving skew. The feature might be computed from data that arrives later, like final order status. First confirm exactly when each feature is available in production. Rebuild the training set using point-in-time values, as they were at prediction time, or drop the feature. A feature store with point-in-time joins and the same feature code for training and serving prevents this. Expect offline scores to drop, but they become honest.
27
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.
28
Write Python code that validates prediction requests with Pydantic before passing them to a model.
Show answer
from pydantic import BaseModel, Field, ValidationError
class LoanRequest(BaseModel):
age: int = Field(ge=18, le=100)
income: float = Field(gt=0)
city: str = Field(min_length=2)
def predict(payload: dict):
try:
req = LoanRequest(**payload)
except ValidationError as e:
return {'error': e.errors()}
return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}
29
Write SQL to compute funnel conversion from visit to cart to purchase.
Show answer
WITH steps AS (
SELECT user_id,
MAX(CASE WHEN event = 'visit' THEN 1 ELSE 0 END) AS visited,
MAX(CASE WHEN event = 'add_to_cart' THEN 1 ELSE 0 END) AS carted,
MAX(CASE WHEN event = 'purchase' THEN 1 ELSE 0 END) AS purchased
FROM events
WHERE event_date >= '2026-10-01'
GROUP BY user_id
)
SELECT SUM(visited) AS visits, SUM(carted) AS carts, SUM(purchased) AS buyers,
ROUND(100.0 * SUM(carted) / NULLIF(SUM(visited), 0), 1) AS visit_to_cart_pct,
ROUND(100.0 * SUM(purchased) / NULLIF(SUM(carted), 0), 1) AS cart_to_buy_pct
FROM steps;
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
You have 50,000 unlabelled images and budget to label only 3,000. How do you use the budget well?
Show answer
Do not pick 3,000 at random. Label a small diverse seed set, using embedding clustering to cover variety, and train a first model. Then use active learning: label the images the model is least confident about or that disagree across models. Use semi-supervised methods like pseudo-labelling high-confidence images, or self-supervised pretraining on all 50,000. Keep a randomly sampled test set so evaluation stays unbiased.
32
Your random forest is accurate but too slow at prediction time for a real-time API. What do you do?
Show answer
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.
33
A Pandas merge suddenly returns many more rows than the left table had. What happened?
Show answer
The join key is probably not unique on the right side, so each left row matches several right rows and the result multiplies. Check with right['key'].duplicated().sum(). Other causes are null or blank keys matching each other after cleaning, or joining on the wrong column. Fix it by deduplicating or aggregating the right table first, and use merge(..., validate='many_to_one') so Pandas raises an error when the assumption breaks.
34
You want to test a new pricing algorithm in a two-sided marketplace where buyers and sellers interact. Why is user-level randomisation a problem and what would you do?
Show answer
Treated and control buyers compete for the same sellers' supply, so a treatment that makes treated buyers buy more takes inventory from control buyers, exaggerating the measured lift. Use designs that limit interference: switchback tests that alternate the whole market between variants over time slots, or randomise by city or region. Analyse at the switch or cluster level with appropriate variance, and accept that these tests need longer to reach power.
35
Your detector finds large objects well but misses small objects like distant people. How do you improve it?
Show answer
Small objects lose detail after downsampling. Train and infer at higher input resolution, or tile large images into overlapping crops. Use a backbone with a feature pyramid so detection happens on high-resolution maps. Tune anchors or assignment settings for small sizes, and oversample images with small objects. Check labelling quality for tiny boxes too. Measure mAP on small objects separately to confirm improvement.
36
Why are NumPy arrays faster than Python lists?
Show answer
They store same-type values in contiguous memory and run vectorised operations in compiled C, avoiding Python loop overhead and per-object type checks.
37
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}')
38
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,)
39
Explain the chi-square test.
Show answer
It checks whether observed category counts differ from expected counts, used for independence between two categorical variables and for goodness of fit.
40
An analyst tested 20 metrics in one experiment and found one significant. Should you trust it, and how do you correct for this?
Show answer
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.
Tips to crack the Autodesk Data Science interview
- Revise C++ or another core language, object-oriented design and data structures, since many desktop products use C++ codebases.
- If you know geometry, graphics or 3D concepts, mention projects that use them, as they suit many Autodesk product teams.
- Try AutoCAD or Fusion student versions to understand the products and speak about them from real experience.
- 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; Autodesk interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Autodesk Data Science interview?
Candidates usually go through 5 stages: Online coding assessment; Technical round 1 (DSA); Technical round 2 (design or C++/domain); Hiring manager 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 Autodesk Data Science interview?
This practice set leans on Statistics and Probability (8), Time Series & Forecasting (5), A/B Testing & Experimentation (5), Python for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Autodesk interviewers ask you to walk through real work.
Are these the exact questions asked at Autodesk?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Autodesk's. They are not a leaked or verified list of past questions.
Where does Autodesk hire Data Science professionals in India?
Autodesk has large teams in Bengaluru, Pune. Openings and locations change often, so check the Autodesk careers page and filter by city and experience.
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