Data validation in CI pipeline in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to run data validation using Great Expectations in the CI pipeline.

MLOps

import great_expectations as ge
import pandas as pd
data = ge.dataset.PandasDataset(pd.read_csv('data.csv'))
results = data.[1]()

Drag options to blanks, or click blank then click option'

Avalidate

Brun

Ctest

Dcheck

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'check' or 'test' instead of 'validate' causes errors.

The validate() method runs the data validation checks defined in Great Expectations.

2fill in blank

medium

Complete the code to load the Great Expectations suite named 'my_suite' for validation.

MLOps

from great_expectations.data_context import DataContext
context = DataContext()
suite = context.get_expectation_suite('[1]')

Drag options to blanks, or click blank then click option'

Adefault_suite

Bvalidation_suite

Csuite1

Dmy_suite

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong suite name causes the pipeline to fail.

The suite name 'my_suite' is used to load the specific expectation suite for validation.

3fill in blank

hard

Fix the error in the command to run data validation in the CI pipeline using Great Expectations CLI.

MLOps

great_expectations [1] --suite my_suite --data-dir ./data

Drag options to blanks, or click blank then click option'

Atest

Bvalidate

Crun

Dcheck

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'check' or 'run' causes command not found errors.

The correct CLI command to run validation is great_expectations validate.

4fill in blank

hard

Fill both blanks to create a Python dictionary comprehension that filters data rows where 'age' is greater than 18.

MLOps

filtered_data = {row['id']: row for row in data if row['[1]'] [2] 18}

Drag options to blanks, or click blank then click option'

Aage

B>

C<

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or wrong key filters incorrect data.

The key 'age' is checked to be greater than 18 using the '>' operator.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase 'name' to 'score' for scores above 50.

MLOps

result = [1]: [2] for item in data if item['score'] [3] 50

Drag options to blanks, or click blank then click option'

Aitem['name'].upper()

Bitem['score']

C>

Ditem['name']

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase names or wrong comparison operator.

The comprehension maps uppercase names to scores where score is greater than 50.