Practice Data Pipeline (14.3.1) - Machine Learning Pipelines and Automation

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9 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does ETL stand for?
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Think of the steps involved in preparing data.
Question 2.
Name a tool used for data manipulation.
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This tool allows you to work with data in Python.
3Question 3.
What does 'ETL' stand for?
- Extract, Transform, Load
- Extract, Transfer, Link
- Execute, Transform, Load
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Think about the steps in data preparation.
4Question 4.
Is Pandas a library in Python used for data manipulation?
- True
- False
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Consider its usage in handling data.
Question 5.
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.
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Think through each phase and example in ETL.
Question 6.
How would you handle missing values in a data pipeline and why is it essential?
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Consider the impact of incomplete data on insights.

5 more questions in this section. Enroll free to practise them all.