Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Accenture hiring for Machine Learning roles
Accenture is a global professional services company offering strategy, consulting, technology, operations and digital services. India hosts a large share of its workforce, delivering software engineering, testing, cloud, data and AI projects for clients worldwide. It hires freshers and experienced professionals in India for associate software engineer, test engineer, data analyst and analytics consultant roles.
For Machine Learning roles, this Accenture practice set focuses most on Data Preprocessing and Feature Engineering (5), Regression Models (5), ML Fundamentals (4), Decision Trees and Ensembles (4). It has 3 easy, 24 medium, 3 hard questions, so you can start easy and work up to the harder ones.
Company type
Consulting
Headquarters
Dublin, Ireland
Main India offices
Bengaluru, Hyderabad, Pune, Mumbai, Gurugram
Accenture Machine Learning interview process
Online assessment (cognitive + technical MCQs)
Coding assessment
Communication assessment
Technical and HR interview
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for AccentureStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
✓ 3 questions free • Unlock the rest in seconds
01
Leadership wants a model in one week, but data quality is poor. How do you handle it?
I would agree on a narrow first version: a simple, explainable baseline built on the cleanest available fields, with clear notes on data gaps and expected accuracy. In parallel I would log specific data issues and their impact. Delivering a working baseline with an honest limitations section builds trust and gives a reference point for improvement in later iterations.
02
A product team wants 'similar products' recommendations with no purchase labels. How would you start?
I would build product embeddings from content: TF-IDF or sentence embeddings of titles and descriptions, plus category and price features. Then use cosine similarity with nearest-neighbour search, such as scikit-learn NearestNeighbors or an approximate index like FAISS for large catalogues. I would review samples with the product team and later add co-view or co-purchase signals once collected.
03
40 percent of values in an important income column are missing. What do you do?
I would study the missingness pattern first, for example whether self-employed applicants skip it. I would add a binary was_missing feature, impute with the median within relevant groups or use IterativeImputer, and compare against models that handle missing values natively, like HistGradientBoosting or XGBoost. I would also raise the issue with the data owner so collection can improve.
Detect with box plots, the IQR rule (below Q1 - 1.5 IQR or above Q3 + 1.5 IQR), z-scores above 3, or models like Isolation Forest. Treatment depends on cause: fix data entry errors, cap values (winsorise), apply log transforms for skewed data, or use robust models and losses. Genuine extreme values, such as large fraud amounts, may be the most important signal and should not be removed blindly.
05
What is a loss function? Name common ones for regression and classification.
A loss function measures how wrong a prediction is for one example, and training minimises its average over the data. Regression commonly uses mean squared error, mean absolute error or Huber loss, which is robust to outliers. Classification uses log loss, also called cross-entropy, and hinge loss for SVMs. The choice should reflect the business cost of errors.
import lightgbm as lgb
m = lgb.LGBMClassifier(n_estimators=5000, learning_rate=0.05,
num_leaves=31, subsample=0.8, subsample_freq=1, colsample_bytree=0.8)
m.fit(X_train, y_train, eval_set=[(X_val, y_val)],
eval_metric='auc', callbacks=[lgb.early_stopping(100)])
print(m.best_iteration_)
The large n_estimators is only an upper limit; early stopping picks the real number.
07
Tell me about a time you balanced model accuracy against a business constraint.
Situation: our lead-scoring model had the best AUC with a large ensemble, but the CRM needed scores within 50 ms. Task: meet latency without losing much quality. Action: I compared a smaller LightGBM model with fewer features and measured both AUC and latency. Result: we lost 0.01 AUC but met the 50 ms limit, and sales teams adopted it because scores appeared instantly.
08
Grid search with 5 hyperparameters is taking two days. How do you speed it up?
Switch to random search or Optuna with pruning, which find good settings with far fewer trials. Narrow ranges using a quick coarse search, tune the most important parameters first, use early stopping for boosting, run trials in parallel with n_jobs, and tune on a stratified sample of the data before a final refit on the full set. HalvingRandomSearchCV is another quick option.
09
When would you use Huber or quantile regression instead of OLS?
Huber regression uses squared loss for small errors and absolute loss for large ones, so it resists outliers while staying smooth. Quantile regression predicts a chosen percentile, such as the 90th percentile of delivery time, which is useful for setting safe promises or prediction intervals. Gradient boosting libraries support both losses for non-linear versions.
10
A model's accuracy dropped 10 percent over three months in production. How do you investigate?
I would first check data pipelines for broken features, nulls or schema changes, then compare current feature distributions with training data using PSI to find drift. I would check whether the label definition or business process changed, and analyse errors by segment. If drift is real, retrain on recent data, possibly with time-weighted samples, and set up automatic drift alerts and scheduled retraining.
11
How do you tune the alpha of Ridge regression using cross-validation?
RidgeCV tests many alphas efficiently; scale features first.
from sklearn.linear_model import RidgeCV
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import numpy as np
m = make_pipeline(StandardScaler(), RidgeCV(alphas=np.logspace(-3, 3, 30)))
m.fit(X_train, y_train)
print(m[-1].alpha_)
LassoCV and ElasticNetCV work the same way.
12
Describe a project where you improved a model's performance significantly.
Situation: a product-return prediction model had 0.68 AUC with logistic regression. Task: improve it for a warehouse pilot. Action: I engineered features like past return rate per customer and category, moved to LightGBM with early stopping, and tuned it with Optuna on time-based folds. Result: AUC rose to 0.81, and the pilot reduced avoidable returns in two categories. I documented each step's gain.
13
What is the difference between Ridge, Lasso and Elastic Net regression?
All add a penalty on coefficient size to reduce overfitting. Ridge uses an L2 penalty (sum of squares), shrinking coefficients toward zero but rarely to zero, and handles multicollinearity well. Lasso uses L1 (sum of absolute values) and can set coefficients exactly to zero, performing feature selection. Elastic Net mixes both, which is useful when there are groups of correlated features.
Situation: my first churn model scored 0.95 AUC in validation but failed in a pilot. Task: find out why. Action: I traced features and found one field updated after customers left, which leaked the label. I removed it, rebuilt features with a strict cut-off date and moved to a time-based split. Result: AUC dropped to 0.78 but held steady in production, and I added a leakage checklist to my workflow.
16
Your model shows 99 percent training accuracy but 72 percent validation accuracy. What will you do?
This is overfitting. I would first check for leakage or a split mismatch, then simplify the model: add regularisation, reduce depth or features, use early stopping, or prune trees. I would gather more data or use augmentation if possible and use cross-validation to confirm the fix. Plotting learning curves shows whether more data would actually help.
17
Why do skewed features often need a log or power transform?
Heavily right-skewed features like income or transaction amount have long tails that can dominate linear models and distance metrics. A log1p, Box-Cox or Yeo-Johnson transform compresses large values and makes the distribution closer to normal, which often improves linear and distance-based models. Yeo-Johnson works with zero and negative values. Tree models usually do not need these transforms.
18
What makes XGBoost, LightGBM and CatBoost different?
XGBoost adds regularisation to the objective, uses second-order gradients and handles missing values natively. LightGBM grows trees leaf-wise and uses histogram binning, making it very fast on large data, though it can overfit small data. CatBoost handles categorical features with ordered target statistics and uses symmetric trees, giving strong defaults with less tuning. All three support GPU training.
19
Find the most similar items using cosine similarity.
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import NearestNeighbors
X = TfidfVectorizer(min_df=2).fit_transform(df['title'])
nn = NearestNeighbors(n_neighbors=6, metric='cosine').fit(X)
dist, idx = nn.kneighbors(X[0])
print(df['title'].iloc[idx[0][1:]].tolist())
The first neighbour is the item itself, so skip it.
20
What does it mean to deploy a machine learning model?
Deployment makes a trained model available to produce predictions for real users or systems. It can be batch, scoring data on a schedule and writing results to a table, or real-time, serving predictions through a REST API, or streaming, scoring events as they arrive. It includes packaging the model with its preprocessing, setting up infrastructure, logging, monitoring and a retraining plan.
21
What is multicollinearity and how do you detect it?
Multicollinearity is high correlation between input features, which makes linear model coefficients unstable and hard to interpret, though predictions may remain fine. Detect it with a correlation matrix or the Variance Inflation Factor, where VIF above 5 to 10 signals a problem. Fix it by dropping one of the correlated features, combining them, using PCA, or applying Ridge regularisation.
Log loss, or cross-entropy, is the negative average log of the probability assigned to the true class. It heavily penalises confident wrong predictions, such as predicting 0.99 for an event that does not happen. It evaluates probability quality, not just ranking, so it suits problems where probabilities drive decisions. Lower is better, and a constant base-rate prediction is the baseline to beat.
24
A demand forecast is consistently under-predicting during festivals. How would you fix it?
The model lacks festival information. I would add features for festival dates such as Diwali or regional holidays, days-to-festival, and past festival uplift for the same item. If the business cost of stock-outs is higher than over-stock, I would use a quantile loss for a higher percentile. Then validate specifically on past festival periods, not just the average error.
25
Why can impurity-based feature importance be misleading?
Impurity-based importance is computed on training data and favours high-cardinality or continuous features, which offer more split points, even if they are noise. It also splits credit between correlated features. Permutation importance on validation data, which measures the score drop when a feature is shuffled, is more reliable, and SHAP values give consistent per-prediction attributions.
26
Your dataset has a city column with 2,000 unique values. How will you encode it?
One-hot would create 2,000 sparse columns, so I would first group rare cities below a frequency threshold into 'Other'. Then options are frequency encoding, out-of-fold target encoding, or mapping cities to useful attributes such as tier, state or population. For boosting models like CatBoost or LightGBM, native categorical handling also works well. I would compare options with cross-validation.
27
How do you keep learning new ML techniques while working full time?
I set a small weekly routine: one paper summary or library release note, and one hands-on experiment on a public dataset. I follow scikit-learn and LightGBM release notes, and I rebuild ideas on a small problem before suggesting them at work. Recently I tried TunedThresholdClassifierCV and later used it to replace a manual threshold script in our project.
28
What is heteroscedasticity and why does it matter?
Heteroscedasticity means the variance of residuals changes with the level of the prediction, such as errors growing for expensive houses. Predictions remain unbiased, but standard errors and p-values become unreliable. Detect it with a residuals versus fitted plot or the Breusch-Pagan test. Fixes include log-transforming the target, weighted least squares, or robust standard errors.
29
You must build a spam classifier quickly with limited compute. What do you choose?
I would use TF-IDF features with a Multinomial Naive Bayes or linear model like logistic regression or LinearSVC. They train in seconds, handle high-dimensional sparse text well and give strong baselines. I would evaluate precision carefully because blocking genuine mail is costly. Only if this is not good enough would I try fine-tuning a small transformer model.
30
Your manager asks for a single number showing the model's business value. What do you give?
I would convert model outcomes into money or time using a cost matrix agreed with the business, for example savings from caught fraud minus the cost of reviewing false alerts. I would compare this against the current process or a rule-based baseline on holdout data and show a range rather than one point. A short note on assumptions keeps the number honest.
Tips to crack the Accenture Machine Learning interview
Practise the communication assessment, which checks spoken English through reading, listening and repeating sentences in a recorded format.
Revise MS Office, cloud basics, networking and pseudocode, as Accenture technical MCQs often cover these general IT topics.
Read about Accenture's focus on cloud, data and AI and be ready to discuss how you would work in client-facing teams.
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; Accenture interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Accenture Machine Learning interview?
Candidates usually go through 4 stages: Online assessment (cognitive + technical MCQs); Coding assessment; Communication assessment; Technical and HR interview. 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 Accenture Machine Learning interview?
This practice set leans on Data Preprocessing and Feature Engineering (5), Regression Models (5), ML Fundamentals (4), Decision Trees and Ensembles (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Accenture interviewers ask you to walk through real work.
Are these the exact questions asked at Accenture?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like Accenture's. They are not a leaked or verified list of past questions.
Where does Accenture hire Machine Learning professionals in India?
Accenture has large teams in Bengaluru, Hyderabad, Pune, Mumbai, Gurugram. Openings and locations change often, so check the Accenture careers page and filter by city and experience.