Practice Data Pipeline - 14.3.1 | 14. Machine Learning Pipelines and Automation | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ETL stand for?

💡 Hint: Think of the steps involved in preparing data.

Question 2

Easy

Name a tool used for data manipulation.

💡 Hint: This tool allows you to work with data in Python.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does 'ETL' stand for?

  • Extract
  • Transform
  • Load
  • Extract
  • Transfer
  • Link
  • Execute
  • Transform
  • Load

💡 Hint: Think about the steps in data preparation.

Question 2

Is Pandas a library in Python used for data manipulation?

  • True
  • False

💡 Hint: Consider its usage in handling data.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a simple data pipeline that collects sales data from multiple online stores, transforms the data to normalize price formats, and loads it into a centralized database.

💡 Hint: Think through each phase and example in ETL.

Question 2

How would you handle missing values in a data pipeline and why is it essential?

💡 Hint: Consider the impact of incomplete data on insights.

Challenge and get performance evaluation