Quest Global Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Quest Global hiring for Data Science roles
Quest Global is an engineering services company headquartered in Singapore with large engineering centres in India. It provides mechanical design, embedded software, digital engineering and manufacturing support to clients in aerospace, energy, healthcare, automotive and semiconductors. In India it hires mechanical, electrical, electronics and software engineers, with centres in cities such as Bengaluru, Thiruvananthapuram and Belagavi.
For Data Science roles, this Quest Global practice set focuses most on Machine Learning (9), Python for Data Science (5), Feature Engineering (4), Deep Learning and NLP (4). It has 23 easy, 17 medium questions, so you can start easy and work up to the harder ones.
Company type
IT services
Headquarters
Singapore
Main India offices
Bengaluru, Thiruvananthapuram, Belagavi, Pune
Quest Global Data Science interview process
Online assessment (aptitude + technical MCQs)
Technical interview
Managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for Quest GlobalStart 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
What is Bayesian A/B testing and how are its results reported?
Bayesian A/B testing starts with a prior on each variant's metric, updates it with observed data and produces a posterior distribution. Instead of a p-value, you report things like the probability that treatment beats control, say 94 percent, and the expected loss if you choose the wrong variant. Business teams find this easier to interpret. Priors must be chosen sensibly, and continuous monitoring still needs discipline about decision rules.
02
Your SQL result does not match the number on the official dashboard. How do you resolve it?
I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.
The ROC curve plots true positive rate against false positive rate across thresholds. AUC is the probability that a random positive is ranked above a random negative; 0.5 is chance.
Information from outside the training window, such as future data or test statistics, enters training and inflates performance. Split first, then fit scalers and encoders on training data only.
05
Why do you apply a log transform to some features?
A log transform compresses large values and spreads small ones, which reduces right skew in features like income, price or page views. This helps linear models and distance-based models that are sensitive to scale and outliers. Use log1p, which is log of 1 plus x, when the feature has zeros. For negative values, a Yeo-Johnson transform works. Tree models generally do not need it.
06
Halfway through a test you discover a bug that broke the treatment for Android users. What do you do?
Stop or pause the affected part, fix the bug and restart the test with fresh data, since mixing broken and fixed periods biases results. Do not simply drop Android users from analysis afterwards, because that can break randomisation balance if the bug affected behaviour or logging. Document the incident, and add pre-launch checks such as running the variant through QA on every platform and monitoring guardrails in the first hours.
It tries candidate splits and picks the one that most reduces impurity — Gini or entropy (information gain) for classification, variance / MSE for regression.
It lets a model weigh which input positions matter most for each output, computing weighted combinations of values from query-key similarity. It is the core of transformers.
Quantisation stores model weights in lower precision, such as 8-bit or 4-bit integers instead of 16-bit floats. A 7-billion parameter model drops from about 14 GB to around 4 GB at 4-bit, so it fits on smaller GPUs or even laptops and runs faster. The cost is some loss in quality, usually small at 8-bit and noticeable on hard tasks at very low bits. Common formats include GPTQ, AWQ and GGUF.
11
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.
12
How does linear regression work and what are its assumptions?
It fits a line minimising squared error. Assumes linearity, independent errors, constant variance (homoscedasticity), normally distributed residuals and no strong multicollinearity.
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.
A measure of asymmetry. Right-skewed data has a long tail on the right (mean greater than median); left-skewed is the opposite. Log transforms often reduce right skew.
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.
An INNER JOIN returns only rows with matching keys in both tables. A LEFT JOIN returns all rows from the left table, with NULLs where the right has no match. A RIGHT JOIN is the mirror of that. A FULL OUTER JOIN returns all rows from both sides, matched where possible. In analytics, LEFT JOIN from a customers table is common so customers with no orders are still counted.
18
Write Pandas code to cap outliers at the 1st and 99th percentile.
# fit limits on training data only, then apply to test
low, high = train['income'].quantile([0.01, 0.99])
train['income_capped'] = train['income'].clip(low, high)
test['income_capped'] = test['income'].clip(low, high)
19
Write torchvision transforms for training and validation of an image classifier using ImageNet normalisation.
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.
21
What are embeddings and vector databases, and why do LLM apps use them?
An embedding model turns text into a vector so that similar meanings are close in space. A vector database stores these vectors with metadata and finds the nearest ones to a query vector quickly using approximate nearest neighbour indexes like HNSW. LLM apps use them for semantic search and retrieval-augmented generation, finding the most relevant documents to put into the prompt. Examples include pgvector, FAISS and dedicated vector stores.
A system prompt is the instruction message placed before user messages that sets the model's role, tone, rules and output format, for example 'You are a support assistant; answer only from the provided policy and reply in JSON'. It shapes every response in the conversation. Keep it clear and specific, version it like code, and do not rely on it alone for security, since users can try to override it through prompt injection.
Trend is the long-term direction of the series, such as steady growth in monthly users. Seasonality is a pattern that repeats at a fixed period, like higher sales every weekend or every festive season. Noise is the random variation left after removing trend and seasonality. Separating these parts, through decomposition, helps you understand the data and pick a model that can capture each component properly.
25
Pandas: find customers whose monthly spend increased for three consecutive months.
Data augmentation creates new training examples by applying realistic changes to existing ones. For images this means flips, rotations, crops and brightness changes. For text it can mean synonym replacement, back-translation or random word dropping. For audio, adding noise or shifting pitch. It improves generalisation when data is limited. Changes must keep the label correct; flipping a digit 6 can turn it into a 9.
27
Write Python code for a two-proportion z-test comparing conversion between control and treatment.
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.
29
What are precision and recall trade-offs in practice?
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.
30
What is the difference between additive and multiplicative seasonality?
With additive seasonality the seasonal swing is a fixed amount, for example weekends always add about 500 orders regardless of the overall level. With multiplicative seasonality the swing is a percentage of the level, so as the business grows the weekend spike grows too. A quick check is to plot the series: if the peaks widen as the trend rises, use multiplicative, or take a log and model it additively.
31
What are type hints in Python and why do data teams use them?
Type hints annotate expected types, for example def clean(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame. Python does not enforce them at runtime, but tools like mypy and IDEs use them to catch mistakes early and give better autocomplete. In data teams they make shared pipeline code easier to read and review. They matter most in production code and libraries, less in quick exploratory notebooks.
Conditional probability is the chance of an event given that another event has happened, written P(A given B) = P(A and B) / P(B). Example: 10 percent of users buy, and 40 percent of users who add to cart buy. So P(buy given cart) is 0.4, much higher than the overall 0.1. It is the base of Bayes' theorem, Naive Bayes and many funnel analyses.
33
Explain precision vs recall. When would you prefer recall?
Precision = TP / (TP + FP). Recall = TP / (TP + FN).
Prefer recall when missing a positive is costly — for example disease screening or fraud detection.
Differencing replaces each value with the change from the previous value: y_t minus y_(t-1). It removes a linear trend and often makes a series stationary. Seasonal differencing subtracts the value one season back, such as y_t minus y_(t-7) for daily data with a weekly cycle. Avoid over-differencing, because it adds noise and negative autocorrelation. In ARIMA, the d parameter is the number of times you difference.
35
What does the axis parameter mean in NumPy and Pandas?
axis tells an operation which direction to work along. axis=0 works down the rows, so df.sum(axis=0) gives one total per column. axis=1 works across columns, so df.sum(axis=1) gives one total per row. In df.drop, axis=1 means drop columns and axis=0 means drop rows. A simple way to remember it: the axis you name is the one that gets collapsed or removed.
36
What is effect size and why should you report it along with a p-value?
Effect size measures how large a difference is, for example the absolute lift in conversion or Cohen's d for means. A p-value only says whether the difference is likely due to chance. With millions of users, a 0.01 percent lift can be significant yet useless for the business. Reporting effect size with a confidence interval tells decision makers both whether the effect is real and whether it is worth acting on.
37
What is a model registry and why do teams use one?
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.
38
What is a baseline model and why should you build one first?
A baseline is the simplest reasonable prediction, such as predicting the most common class, last week's value or a basic logistic regression. It sets the minimum score any real model must beat. Without it, a 75 percent accuracy can look good when predicting the majority class already gives 80 percent. Baselines also catch pipeline bugs early and show whether extra complexity is worth it.
39
What is the difference between apply, map and applymap in Pandas?
map works element-wise on a Series, apply works on a Series or along a DataFrame axis, and applymap (now DataFrame.map) applies a function to every cell.
40
When would you use MAE, MSE or RMSE for a regression model?
MAE is the average absolute error; it is easy to explain and less sensitive to outliers. MSE squares errors, so large errors are punished heavily, and it is smooth for optimisation. RMSE is the square root of MSE, so it is in the same units as the target while still punishing big misses. Use RMSE when large errors are very costly, like stock-out forecasts, and MAE when outliers should not dominate.
Tips to crack the Quest Global Data Science interview
Revise core subjects from your engineering branch, such as machine design, electronics or programming, depending on the role applied for.
Be ready to discuss tools you have used, such as CAD software, MATLAB or a programming language, with concrete project examples.
Read about Quest Global's industries like aerospace and energy so you can explain why engineering services appeal to you.
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; Quest Global interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Quest Global Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + technical MCQs); Technical interview; Managerial 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 Quest Global Data Science interview?
This practice set leans on Machine Learning (9), Python for Data Science (5), Feature Engineering (4), Deep Learning and NLP (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Quest Global interviewers ask you to walk through real work.
Are these the exact questions asked at Quest Global?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Quest Global's. They are not a leaked or verified list of past questions.
Where does Quest Global hire Data Science professionals in India?
Quest Global has large teams in Bengaluru, Thiruvananthapuram, Belagavi, Pune. Openings and locations change often, so check the Quest Global careers page and filter by city and experience.