Learn what data wrangling is, its key processes, and why it’s essential for analytics and AI. Perfect for preparing for Google Cloud Platform certification exams.
Table of Contents
Question
What is data wrangling?
A. The cleaning and sorting of raw data into data that can be analyzed
B. The process of assigning values to elements of data in a pipeline
C. The procedure of executing a task with data queried from a dataset
D. A way of representing a flow of data
Answer
A. The cleaning and sorting of raw data into data that can be analyzed
Explanation
Data wrangling refers to the process of transforming raw, unorganized data into a structured and usable format suitable for analysis. It involves several key steps, including:
- Cleaning Data: Correcting errors, handling missing values, removing duplicates, and addressing inconsistencies in the dataset.
- Structuring Data: Organizing data into formats that are easier to analyze, such as tabular structures.
- Enriching Data: Adding new information or features to improve the dataset’s quality and relevance for analysis.
- Validating Data: Ensuring accuracy and consistency through checks and validation rules.
- Publishing Data: Preparing the cleaned and validated dataset for downstream applications like machine learning or reporting.
This process ensures high-quality data that is essential for reliable analytics, decision-making, and AI model development. It is a foundational skill for professionals working with large datasets in cloud environments like Google Cloud Platform.
Why Option A is Correct
Option A accurately captures the essence of data wrangling, which focuses on cleaning and organizing raw data to make it analyzable. Other options describe different aspects of data pipelines or workflows but do not fully encompass the comprehensive process of data wrangling:
Option B refers to assigning values in a pipeline, which is a narrower task within data processing.
Option C describes executing tasks with queried data but lacks the broader scope of cleaning and organizing.
Option D refers to representing data flow, which aligns more with visualization or pipeline design rather than wrangling.
Mastering this concept is crucial for passing Google Cloud Platform certification exams focused on analytics and AI.
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