Скачать или смотреть Advanced Python Interview Questions for Data Analysts & Scientists: MLflow, Model Drift, Dask & More

Here are 5 advanced Python interview questions for data analysts and scientists with detailed answers and code examples:

1️⃣ How do you use MLflow for experiment tracking and model management in Python?

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle.

It allows you to track experiments, package code into reproducible runs, and share models.

Example:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

Start an MLflow run
with mlflow.start_run():
Train a model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

Log model and parameters
mlflow.log_param("n_estimators", 100)
mlflow.sklearn.log_model(model, "random_forest_model")
accuracy = model.score(X_test, y_test)
mlflow.log_metric("accuracy", accuracy)
print("Logged Accuracy:", accuracy)

This setup helps in tracking, comparing, and managing different experiment runs.

2️⃣ How do you detect and mitigate model drift using Python tools?

Model drift occurs when the statistical properties of the target variable or features change over time.

Use monitoring tools like Evidently AI or custom statistical tests (e.g., Kolmogorov-Smirnov test) to detect drift.

Example using a KS test:

from scipy.stats import ks_2samp
import numpy as np

Simulated historical and current data
historical = np.random.normal(loc=0, scale=1, size=1000)
current = np.random.normal(loc=0.1, scale=1, size=1000)

statistic, p_value = ks_2samp(historical, current)
print("KS Statistic:", statistic, "p-value:", p_value)

A low p-value indicates significant differences, signaling drift that may require model retraining or adjustment.

3️⃣ How do you implement distributed computing for data analysis using Dask?

Dask scales Python libraries like pandas and NumPy for large datasets by parallelizing operations and processing data in chunks.

Example:

import dask.dataframe as dd

Read a large CSV in chunks
df = dd.read_csv("large_dataset.csv")

Perform operations similar to pandas; computations are lazy until computed
df_filtered = df[df['column'] v 0]
result = df_filtered.describe().compute()
print(result)

Dask provides a pandas-like API and automatically distributes computation across cores or a cluster.

4️⃣ How do you perform automated data quality assessments using Great Expectations in Python?

Great Expectations is an open-source tool that validates, documents, and profiles your data to ensure its quality.

It uses “expectations” to define data quality rules.

Example:

import great_expectations as ge
import pandas as pd

Create a DataFrame
df = pd.DataFrame({
"id": [1, 2, 3, 4],
"value": [10, 20, None, 40]
})

Convert DataFrame to a Great Expectations DataFrame
ge_df = ge.from_pandas(df)

Define expectations
ge_df.expect_column_values_to_not_be_null("id")
ge_df.expect_column_values_to_be_between("value", min_value=5, max_value=50)

Validate data
results = ge_df.validate()
print(results)

This process automatically detects data anomalies and ensures consistency.

5️⃣ How do you implement multi-armed bandit algorithms for online optimization in Python?

Multi-armed bandits balance exploration and exploitation in decision-making, such as dynamically optimizing recommendations or pricing.

Use libraries like MABWiser or custom implementations.

Example using a simple epsilon-greedy strategy:

import numpy as np

def epsilon_greedy(arms, rewards, epsilon=0.1):
if np.random.rand() v epsilon:
return np.random.choice(len(arms)) # Explore
else:
return np.argmax(rewards) # Exploit

Simulated arms and reward estimates
arms = ['A', 'B', 'C']
rewards = np.array([0.2, 0.5, 0.3])

chosen_arm = arms[epsilon_greedy(arms, rewards)]
print("Chosen arm:", chosen_arm)

This algorithm is essential for online optimization tasks where continuous learning and adaptation are needed.

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