Qualcomm Data Science Interview Questions &amp; Answers

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Gradients shrink as they pass through many layers (for example with sigmoid), so early layers barely learn. ReLU, residual connections and good initialisation help.

How do you deal with high-cardinality categorical features?

Qualcomm Feature EngineeringMedium

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Use target or frequency encoding with cross-validation, hashing, embeddings or grouping rare categories, instead of huge one-hot matrices.

Write pytest unit tests for a feature function that computes days since last purchase.

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import pandas as pd from features import days_since_last_purchase def test_basic_case(): df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.loc[1] == 5 def test_no_future_orders_used(): df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.empty

What is the receptive field of a neuron in a CNN?

Qualcomm Computer VisionMedium

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The receptive field is the region of the input image that can influence one activation. Early layers see small patches like edges, and deeper layers see larger regions, enabling recognition of whole objects. It grows with kernel size, depth, stride and pooling, and dilated convolutions increase it without extra parameters. If the receptive field is smaller than the objects you care about, the network cannot capture their full context.

A new checkout converts better on both mobile and desktop, but worse overall. How is that possible?

Qualcomm Statistics and ProbabilityHard

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Simpson's paradox happens when a trend in several groups reverses when the groups are combined. Example: a new checkout converts better on both mobile and desktop, but worse overall, because most of its traffic came from mobile, which has lower conversion anyway. The mix of groups, not the treatment, drives the total. The lesson is to check segment-level results and confounding variables before drawing conclusions from aggregated numbers.

How do you profile and speed up slow Python code?

Qualcomm Python for Data ScienceMedium

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Measure first with cProfile or timeit, then replace loops with NumPy or Pandas vectorised calls, use built-ins, caching and better data structures.

Write a basic PyTorch training loop for one epoch.

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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)

What is recursive feature elimination?

Qualcomm Feature EngineeringMedium

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Recursive feature elimination trains a model, ranks features by importance or coefficient size, removes the weakest ones, and repeats until the desired number remains. RFECV picks the number of features using cross-validation. It considers features together rather than one at a time. The downsides are high compute cost on large data and unstable results when features are correlated, so check stability across folds.

Write Python code to group a list of order dictionaries by city and sum the amounts without Pandas.

Qualcomm Python for Data ScienceEasy

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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}

What is the F1 score and when can it be misleading?

Qualcomm Machine LearningEasy

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F1 is the harmonic mean of precision and recall, so it is high only when both are high. It is useful for imbalanced classes where accuracy hides poor performance. It can mislead because it ignores true negatives and treats precision and recall as equally important. If missing a fraud case costs far more than a false alarm, use an F-beta score with beta above 1, or choose metrics based on real costs.

What should you log for a production prediction service?

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For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.

What does CI/CD mean for machine learning projects?

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Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.

What is a p-value?

Qualcomm Statistics and ProbabilityMedium

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The probability of seeing a result at least as extreme as the observed one if the null hypothesis were true. A small p-value is evidence against the null, not proof that the alternative is true.

What is the difference between a subquery and a correlated subquery?

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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.

How do you turn text columns into features for a tabular model?

Qualcomm Feature EngineeringMedium

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Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.

Your RAG chatbot often answers using the wrong documents. How do you fix it?

Qualcomm Generative AI and LLMsHard

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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.

How do you pull large data from a SQL database into Pandas efficiently?

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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.

Tree feature importance ranks an ID-like numeric column near the top. Why, and which importance method would you trust instead?

Qualcomm Feature EngineeringHard

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Impurity-based importance, the default in tree models, sums how much each feature reduces impurity across splits. It is fast but biased toward high-cardinality and continuous features, and it is computed on training data. Permutation importance shuffles one feature at a time on validation data and measures the drop in score, so it reflects real predictive value. Both can understate correlated features, because the model can lean on the copy.

Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.

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from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import brier_score_loss base = HistGradientBoostingClassifier() cal = CalibratedClassifierCV(base, method='isotonic', cv=5) cal.fit(X_train, y_train) prob = cal.predict_proba(X_test)[:, 1] print('Brier:', brier_score_loss(y_test, prob))

Write SQL to show each customer's first order date, last order date and the gap between them in days.

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SELECT customer_id, MIN(order_date) AS first_order, MAX(order_date) AS last_order, MAX(order_date) - MIN(order_date) AS days_between, COUNT(*) AS orders FROM orders GROUP BY customer_id ORDER BY days_between DESC; -- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines

Write Python code to compute the Population Stability Index for a feature to detect drift.

Qualcomm MLOps and Model DeploymentHard

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import numpy as np def psi(expected, actual, bins=10): edges = np.quantile(expected, np.linspace(0, 1, bins + 1)) edges[0], edges[-1] = -np.inf, np.inf e = np.histogram(expected, edges)[0] / len(expected) a = np.histogram(actual, edges)[0] / len(actual) e, a = np.clip(e, 1e-6, None), np.clip(a, 1e-6, None) return float(np.sum((a - e) * np.log(a / e))) # rule of thumb: <0.1 stable, 0.1-0.25 watch, >0.25 investigate

What is PCA and when do you use it?

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It projects data onto orthogonal directions of maximum variance to reduce dimensions, remove correlation and speed up models, at some cost of interpretability.

What is the difference between parametric and non-parametric tests?

Qualcomm Statistics and ProbabilityMedium

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Parametric tests assume a distribution (usually normal) and use means; non-parametric tests such as Mann-Whitney use ranks and make fewer assumptions.

Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.

Qualcomm A/B Testing & ExperimentationMedium

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from statsmodels.stats.power import NormalIndPower from statsmodels.stats.proportion import proportion_effectsize baseline, target = 0.05, 0.055 effect = proportion_effectsize(target, baseline) # Cohen's h n = NormalIndPower().solve_power( effect_size=effect, alpha=0.05, power=0.8, ratio=1.0, alternative='two-sided' ) print(round(n)) # about 31,000 users per group

What is the Augmented Dickey-Fuller test?

Qualcomm Time Series & ForecastingMedium

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The ADF test checks whether a series has a unit root, which means it is non-stationary. The null hypothesis is that a unit root exists. A small p-value, say below 0.05, lets you reject the null and treat the series as stationary. It is often paired with the KPSS test, whose null is the opposite, stationarity. Using both gives a more reliable conclusion before choosing how much to difference.

Write SQL to calculate month-over-month revenue growth.

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WITH monthly AS ( SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue FROM orders GROUP BY 1 ) SELECT month, revenue, LAG(revenue) OVER (ORDER BY month) AS prev_revenue, ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month)) / NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct FROM monthly;

How would you design an ML platform for a team of 20 data scientists?

Qualcomm MLOps and Model DeploymentHard

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I would start from the team's pain points, then provide shared building blocks: a standard project template, managed notebooks with access to the warehouse, experiment tracking and a model registry, an orchestrator like Airflow for pipelines, a feature store if real-time use cases exist, a standard serving path using containers, and default monitoring. Prefer managed services over custom builds, and add self-serve documentation so teams do not depend on one platform engineer.

How do you handle dates and time zones in Pandas?

Qualcomm Python for Data ScienceMedium

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Convert strings with pd.to_datetime, giving a format argument for speed and safety. Use the .dt accessor to pull year, month, weekday or hour. For time zones, first tz_localize naive timestamps to their real source zone, then tz_convert to a common zone such as UTC or Asia/Kolkata. Store data in UTC and convert only for reporting. Mixing naive and aware timestamps causes errors, so keep one convention across the pipeline.

What is the difference between apply, map and applymap in Pandas?

Qualcomm Python for Data ScienceMedium

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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.

How would you structure a data science codebase so notebooks do not become unmaintainable?

Qualcomm Python for Data ScienceHard

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Keep notebooks for exploration and reporting only. Move reusable logic into a Python package with modules for data loading, features, training and evaluation, and import it into notebooks. Keep settings in config files, write unit tests for feature functions, and use pre-commit with a formatter and linter. Track experiments in a tool like MLflow. A clear folder template and code reviews keep the team consistent as the project grows.

What is batch normalisation?

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It normalises layer inputs per mini-batch and then rescales them, stabilising and speeding up training and allowing higher learning rates.

How do you evaluate clustering when you have no labels?

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Use internal metrics: the silhouette score compares how close a point is to its own cluster versus the nearest other cluster, and the Davies-Bouldin index measures cluster overlap. The elbow method looks at within-cluster variance. But the real test is usefulness: are clusters stable across random seeds and samples, can the business describe them, and do they behave differently on outcomes like spend or churn?

What is transfer learning?

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Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.

What is MAPE and when is it a poor metric for forecast accuracy?

Qualcomm Time Series & ForecastingMedium

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MAPE is the mean of absolute errors divided by actual values, expressed as a percentage. It is easy to explain to business teams, but it breaks when actuals are zero or near zero, because the percentage explodes. It also penalises over-forecasts more than under-forecasts and weights small items heavily. For intermittent or low-volume series prefer MAE, weighted MAPE or MASE, which compares error to a naive baseline.

Write Pandas code to compute each customer's average order value excluding the current order.

Qualcomm Feature EngineeringHard

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g = df.groupby('customer_id')['amount'] total = g.transform('sum') count = g.transform('count') # Leave-one-out mean; NaN when it is the customer's only order df['avg_other_orders'] = (total - df['amount']) / (count - 1) df.loc[count == 1, 'avg_other_orders'] = float('nan') # For time-aware features, use only earlier orders instead

What is the binomial distribution and when do you use it?

Qualcomm Statistics and ProbabilityEasy

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The binomial distribution gives the probability of getting k successes in n independent trials, each with the same success probability p. Its mean is n times p and its variance is n times p times (1 minus p). You use it for counts of yes-or-no outcomes, such as how many of 1,000 visitors convert or how many of 50 parts are defective. For large n it is well approximated by a normal distribution.

What is the difference between stemming and lemmatisation?

Qualcomm Deep Learning and NLPEasy

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Both reduce words to a base form. Stemming chops suffixes using rules, so studies becomes studi; it is fast but can produce non-words. Lemmatisation uses vocabulary and grammar to return the real dictionary form, so studies becomes study and better becomes good. Lemmatisation is more accurate but slower. With modern transformer models, neither is usually needed because subword tokenisers handle word forms.

Write Python code that validates prediction requests with Pydantic before passing them to a model.

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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])}

Training a transformer on a GPU fails with an out-of-memory error. What can you do?

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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.

What are precision and recall trade-offs in practice?