🤖 AI &amp; ML

How do you fetch JSON from a REST API and parse it?

This tests practical fluency with HTTP mechanics and JSON deserialization. A strong answer names the method, URL, and headers; checks the status code; then parses with r.json() or json.loads. A red flag is skipping error handling or confusing GET with POST.

Find customers who have not placed any orders

Tests SQL anti-join logic. Great answers show two paths: LEFT JOIN plus IS NULL on orders.customer_id, or NOT EXISTS, and mention NULL safety with NOT IN. Red flag: INNER JOIN with DISTINCT, which silently drops customers without orders.

How do you analyze and reduce large pandas DataFrame memory usage?

This tests in-memory representation and systematic optimization. Start with df.info(memory_usage='deep'), downcast numerics with to_numeric, convert low-cardinality strings to category, and use nullable dtypes.

Process a 50GB CSV with only 16GB RAM

Chunk with read_csv chunksize, filter columns via usecols, downcast int64 to int32/int16, skip rows.

Convert string timestamps to datetime and extract day of week

This tests pandas datetime parsing and accessor fluency. A strong answer uses pd.to_datetime, assigns the result, then extracts the day via .dt.day_name() or .dt.dayofweek. Red flag: manual string splitting or Python loops instead of vectorized ops.

Calculate total and average sales per region in pandas

Tests split-apply-combine fluency. A strong answer groups by Region then calls agg with a dict or named aggregation to return sum and mean of Sales_Amount together. Red flag: chaining separate groupby calls or looping rows manually.

What is vectorization in NumPy and pandas?

Tests if you know why NumPy operations beat Python loops via contiguous memory and C-level SIMD. A strong answer defines vectorization as array-wide operations without explicit loops, contrasts a ufunc to a for-loop, and cites interpreter overhead removal.

How would you combine customer and transaction DataFrames and describe join types?

This tests relational merging and join semantics in pandas. Answer: use pd.merge on customer_id, then groupby sum; describe inner, left, right, and outer joins by key preservation. Red flag: proposing concat without keys or conflating inner and left joins.

easy1 min read

Most efficient way to convert list of dicts to pandas DataFrame

Tests knowledge of DataFrame construction versus repeated row-wise assembly. Answer: pass the list directly to pd.DataFrame(data); it aligns missing keys as NaN. Red flag: recommending iterative DataFrame building.

Describe strategies for handling missing values in pandas DataFrames

Tests practical judgment on cleaning trade-offs. Good answers contrast dropna when data is abundant against fillna imputation to preserve rows, noting bias risk. Red flag: prescribing one fix without asking why values are missing or what the model needs.

How do you determine sample size for a conversion lift experiment?

Tests fluency with statistical experiment design. A strong answer frames N as a function of alpha, power, baseline rate, and MDE, noting that shrinking the MDE or raising power inflates N. Red flag: picking N from traffic instead of risk tolerance.

Why is 99% accuracy misleading with 1% disease prevalence?

Tests class imbalance intuition. A strong answer notes that an all-negative classifier hits 99% accuracy, then names precision, recall, F1, and AUC-PR to expose false negatives and false positives. Red flag: claiming accuracy is fine after rebalancing.

Describe the bias-variance tradeoff and how model complexity affects bias and variance

More complexity cuts bias but boosts variance via overfitting; test error forms a U.

Explain the Central Limit Theorem and its importance for hypothesis testing

This tests whether you know why sample means from skewed populations tend toward normal as size grows, enabling tests. A strong answer covers mean convergence to normal and standard error. Red flag: claiming the CLT works for small samples or single points.

What is a p-value? Interpret p = 0.03 at alpha = 0.05.

Tests frequentist testing and p-value misinterpretations. Define p-value as the probability of data this extreme under the null; since 0.03 < 0.05, reject the null at 5%. Never say it is the probability the null is false or the result is due to chance.

What are your null and alternative hypotheses for this A/B test?

This tests translating a directional business question into statistical hypotheses. A strong answer states H0 as no improvement (or a point-null equality for a specified test) and H1 as green outperforming blue. A red flag is framing H0 as 'blue is better' or changing the tail after seeing data.

How would you build and validate a proxy target for employee burnout?

Combine survey scales with behavioral signals such as off-hours logins and PTO drops; validate via convergent and predictive validity against attrition.

intermediate3 min read

How do you frame high-value customer identification as classification versus regression?

Tests mapping a business goal to a defensible target. Outline: define value and action, then contrast regression predicting spend versus classification predicting tiers. Red flag: picking models before fixing the label or the campaign action.

How would you recommend launching a checkout flow with mixed A/B metrics?

This tests multi-metric trade-offs. A strong answer tags conversion as success and AOV as a guardrail, estimates net revenue impact, and frames decision as a risk-managed business choice. A red flag is demanding all metrics win or ignoring business context.