Python Interview Questions for Data Analysts & Scientists: AutoML, BigQuery, GPU Acceleration & More

Here are 5 advanced Python interview questions with detailed answers and code examples:

1️⃣ How do you implement AutoML workflows in Python using TPOT or Auto‑Sklearn?

AutoML in Python can be implemented using libraries like TPOT, which uses genetic programming to explore and optimize machine learning pipelines, including preprocessing, model selection, and hyperparameter tuning

Auto‑Sklearn provides a similar interface, leveraging Bayesian optimization to efficiently search for the best model configurations

from tpot import TPOTClassifier

iris = load_iris()
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)

2️⃣ How do you integrate Python with Google BigQuery for large‑scale data analytics?

You can integrate Python with BigQuery using the pandas-gbq package to load DataFrames directly into BigQuery tables

Alternatively, use the google-cloud-bigquery client library to run SQL queries and retrieve results as pandas DataFrames

import pandas_gbq

project_id = "my-gcp-project"

3️⃣ How do you use GPU‑accelerated data processing with RAPIDS cuDF in Python?

RAPIDS cuDF offers a pandas‑like API that runs DataFrame operations on NVIDIA GPUs for massive speedups

import pandas as pd

print(summary)

4️⃣ How do you apply explainable AI using the ELI5 library in Python?

ELI5 provides a unified API to interpret both white‑box and black‑box models through techniques like permutation importance and feature weights visualization

import eli5

iris = load_iris()
# Global feature importance

5️⃣ How do you perform time series forecasting using Facebook Prophet in Python?

Prophet is an open‑source forecasting tool from Facebook designed for easily modeling time series data with multiple seasonality and holiday effects

You prepare your data in a DataFrame with columns ds (date) and y (value), fit the Prophet model, and generate future predictions with the make_future_dataframe and predict methods

from fbprophet import Prophet
import pandas as pd

m = Prophet()
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())

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