Data drift detection basics in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Data Drift

What does data drift mean in the context of machine learning models?

A A change in the input data distribution over time that can affect model performance

B A sudden failure of the model due to hardware issues

C An increase in the size of the training dataset

D A change in the model's architecture during training

💡 Hint

Think about how the data the model sees might change after deployment.

Data drift refers to changes in the input data's statistical properties over time. This can cause the model to perform worse because it was trained on different data.

💻 Command Output

intermediate

Detecting Data Drift with a Python Library

Given the following Python code snippet using the alibi-detect library to detect data drift, what will be the output if the new data distribution is significantly different from the reference data?

MLOps

from alibi_detect.cd import KSDrift
import numpy as np

# Reference data: normal distribution
ref_data = np.random.normal(0, 1, 1000)

# New data: shifted mean
new_data = np.random.normal(2, 1, 1000)

cd = KSDrift(ref_data)
drift_result = cd.predict(new_data)
print(drift_result['data']['is_drift'])

A None

B False

C Raises TypeError

D True

💡 Hint

KSDrift uses the Kolmogorov-Smirnov test to detect if distributions differ.

The new data has a different mean, so the KS test will detect drift and return True.

❓ Configuration

advanced

Configuring a Data Drift Monitoring Pipeline

Which of the following YAML configurations correctly sets up a data drift monitoring job that runs daily and alerts if drift is detected?

A

schedule: hourly
monitor:
  type: data_drift
  alert: false
  threshold: 0.5

B

schedule: daily
monitor:
  type: data_drift
  alert: true
  threshold: 0.05

C

schedule: daily
monitor:
  type: data_drift
  alert: yes
  threshold: 0.01

D

schedule: daily
monitor:
  type: drift_detection
  alert: true
  threshold: 0.05

💡 Hint

Check for correct keys and valid values for alert and threshold.

Option B uses correct keys and values: 'daily' schedule, 'data_drift' type, alert enabled as boolean, and a reasonable threshold.

❓ Troubleshoot

advanced

Troubleshooting False Negatives in Data Drift Detection

A data drift detection system is not alerting even though the input data has clearly changed. Which of the following is the most likely cause?

A The monitoring job is running too frequently

B The model was retrained recently

C The drift detection threshold is set too high, making it insensitive to changes

D The input data format changed but the detection system ignores format

💡 Hint

Consider how sensitivity settings affect detection.

If the threshold is too high, small or moderate changes won't trigger alerts, causing false negatives.

🔀 Workflow

expert

Order of Steps in Data Drift Detection Workflow

Arrange the following steps in the correct order for a typical data drift detection workflow.

A 1,2,3,4

B 1,3,2,4

C 2,1,3,4

D 3,1,2,4

💡 Hint

Think about the logical flow from data collection to alerting.

The workflow starts with collecting reference data, then new data, followed by testing, and finally alerting if drift is found.

Practice

1. What is the main purpose of data drift detection in machine learning?

easy

A. To check if new data differs significantly from the training data

B. To improve the speed of model training

C. To reduce the size of the training dataset

D. To increase the number of features in the model

Solution

1. Step 1: Understand data drift concept

   Data drift detection is about monitoring if new incoming data changes compared to the data used to train the model.

2. Step 2: Identify the purpose

   This helps ensure the model stays accurate by alerting when data changes too much.

3. Final Answer:

   To check if new data differs significantly from the training data -> Option A

4. Quick Check:

   Data drift = detecting data changes [OK]

Hint: Data drift means new data differs from old data [OK]

Common Mistakes:

• Confusing data drift with model training speed
• Thinking data drift reduces dataset size
• Believing data drift adds features

2. Which of the following is a correct Python code snippet to check data drift using the Kolmogorov-Smirnov test on two datasets data_train and data_new?

easy

A. from scipy.stats import ks_test
result = ks_test(data_train, data_new)
print(result.pvalue)

B. from scipy.stats import ks_2samp
result = ks_2samp(data_train, data_new)
print(result.pvalue)

C. from sklearn.drift import ks_test
result = ks_test(data_train, data_new)
print(result.pvalue)

D. import stats
result = stats.ks_test(data_train, data_new)
print(result.pvalue)

Solution

1. Step 1: Identify correct import and function

   The Kolmogorov-Smirnov test is in scipy.stats as ks_2samp.

2. Step 2: Check function usage

   Calling ks_2samp(data_train, data_new) returns a result with pvalue attribute.

3. Final Answer:

   from scipy.stats import ks_2samp result = ks_2samp(data_train, data_new) print(result.pvalue) -> Option B

4. Quick Check:

   Correct function and import = from scipy.stats import ks_2samp result = ks_2samp(data_train, data_new) print(result.pvalue) [OK]

Hint: Use scipy.stats.ks_2samp for data drift test [OK]

Common Mistakes:

• Using wrong module or function name
• Trying to import non-existent ks_test
• Confusing sklearn with scipy for this test

3. Given the following Python code to detect data drift, what will be the output if data_train = [1, 2, 3, 4, 5] and data_new = [1, 2, 3, 4, 10]?

from scipy.stats import ks_2samp
result = ks_2samp(data_train, data_new)
print(round(result.pvalue, 2))

medium

A. 0.87

B. 0.05

C. 0.01

D. 1.00

Solution

1. Step 1: Understand the test and data

   The Kolmogorov-Smirnov test compares distributions. Here, only one value differs (5 vs 10).

2. Step 2: Interpret p-value meaning

   A high p-value (close to 1) means no significant difference, low means drift detected.

3. Final Answer:

   0.87 -> Option A

4. Quick Check:

   Small difference gives high p-value = 0.87 [OK]

Hint: Small data changes give high p-value (no drift) [OK]

Common Mistakes:

• Assuming any difference means low p-value
• Confusing p-value with test statistic
• Rounding errors in output

4. You wrote this code to detect data drift but get an error: AttributeError: module 'scipy.stats' has no attribute 'ks_test'. What is the fix?

import scipy.stats as stats
result = stats.ks_test(data_train, data_new)
print(result.pvalue)

medium

A. Use stats.kstest instead of ks_test

B. Import ks_test from scipy.stats explicitly

C. Change ks_test to ks_2samp in the code

D. Update scipy package to latest version

Solution

1. Step 1: Identify the error cause

   The error says ks_test does not exist in scipy.stats.

2. Step 2: Use correct function name

   The correct function for two-sample KS test is ks_2samp, not ks_test.

3. Final Answer:

   Change ks_test to ks_2samp in the code -> Option C

4. Quick Check:

   Function name must be ks_2samp [OK]

Hint: Use ks_2samp, not ks_test, for two-sample KS test [OK]

Common Mistakes:

• Trying to import non-existent ks_test
• Using one-sample test function by mistake
• Ignoring error message details

5. You want to monitor data drift for multiple features in your dataset. Which approach best helps detect drift over time and alert you when it happens?

hard

A. Ignore data drift and focus on model accuracy metrics only

B. Retrain the model daily without checking data changes

C. Increase the model complexity to handle any data changes automatically

D. Run a statistical test like KS test on each feature periodically and trigger alerts if p-value is below threshold

Solution

1. Step 1: Understand monitoring multiple features

   Checking each feature for drift helps catch changes in data distribution over time.

2. Step 2: Use statistical tests and alerts

   Applying tests like KS test periodically and alerting on low p-values ensures timely detection.

3. Final Answer:

   Run a statistical test like KS test on each feature periodically and trigger alerts if p-value is below threshold -> Option D

4. Quick Check:

   Periodic tests + alerts = best drift monitoring [OK]

Hint: Test features regularly and alert on low p-values [OK]

Common Mistakes:

• Retraining blindly without drift checks
• Ignoring drift and trusting accuracy alone
• Assuming complex models fix drift automatically