Discover the correct answer to “What is data cleaning?” as defined for the Google Cloud certification exam. Learn how this process ensures accurate analytics and AI outcomes.
Table of Contents
Question
What is data cleaning?
A. The process of assigning values to elements of data in a pipeline
B. The sorting of raw data into data that can be analyzed
C. The process of correcting or removing corrupt or inaccurate records from a set of data
D. A way of representing a flow of data
Answer
C. The process of correcting or removing corrupt or inaccurate records from a set of data
Explanation
Data cleaning, also known as data cleansing or scrubbing, is the process of identifying and correcting (or removing) corrupt, inaccurate, duplicated, or irrelevant records within a dataset to improve data quality. It ensures consistency, completeness, and reliability in data used for analysis, machine learning, and decision-making.
Why Option C is Correct
Core Definition
Data cleaning focuses on resolving errors such as typos, missing values, duplicates, and invalid entries. For example, fixing a postal code formatted incorrectly or removing duplicate customer records.
Contrast with Other Options
Option A (assigning values) refers to data transformation, not cleaning.
Option B (sorting raw data) relates to data organization, not error correction.
Option D (data flow representation) describes data pipelines, not cleaning.
Certification Relevance
Google Cloud’s ML Engineer exam emphasizes clean data as foundational for building reliable AI models. Dirty data can lead to biased or inaccurate results, making cleaning critical for tasks like model training.
Steps in Data Cleaning
- Validation: Enforcing strict rules (e.g., rejecting invalid email formats).
- Standardization: Harmonizing formats (e.g., converting “St” to “Street”).
- De-duplication: Removing redundant entries.
- Error Correction: Fixing outliers, missing values, or syntax errors.
By ensuring clean data, organizations improve AI accuracy, streamline workflows, and enable trustworthy analytics59. For Google Cloud certification, understanding this process is essential for designing scalable, ethical ML solutions.
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