Mastering Data Cleaning Techniques

Mastering Data Cleaning Techniques

12th Grade

8 Qs

quiz-placeholder

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Mastering Data Cleaning Techniques

Mastering Data Cleaning Techniques

Assessment

Quiz

Education

12th Grade

Easy

Created by

Kishore P

Used 1+ times

FREE Resource

8 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary goal of data cleaning?

To increase data redundancy.

To ensure data accuracy and quality.

To enhance data visualization.

To simplify data storage.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does outlier detection help to identify in a dataset?

Trends over time in the dataset

Common patterns in the data

Anomalies or unusual data points in a dataset.

The average value of the dataset

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it important to standardize data formats during cleaning?

To make the data visually appealing for presentations.

To ensure data is stored in a specific database format.

It is important to standardize data formats to ensure consistency and accuracy in data analysis.

To reduce the size of the data files.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of removing duplicates in a dataset?

To make data more complex for analysis.

To ensure data integrity and improve analysis accuracy.

To increase the size of the dataset.

To eliminate all data points from the dataset.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can you identify inconsistencies in categorical data?

Inconsistencies in categorical data can be identified by checking for duplicates, missing values, invalid categories, typos, and unexpected distributions.

Checking for numerical values

Validating data against a database

Analyzing time series data

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What role does data validation play in the data cleaning process?

Data validation plays a crucial role in identifying and correcting errors in the data before cleaning.

Data validation has no impact on data accuracy.

Data validation is used to enhance data visualization.

Data validation is only necessary after data cleaning.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the difference between data cleaning and data transformation?

Data transformation is solely about data visualization.

Data cleaning and transformation are the same process.

Data cleaning is only about removing duplicates.

Data cleaning focuses on improving data quality, while data transformation focuses on changing data format or structure.

8.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the impact of poor data quality on analysis outcomes?

Poor data quality results in inaccurate analysis outcomes, leading to misleading insights and poor decision-making.

Data quality has no effect on decision-making.

High data quality guarantees perfect insights.

Poor data quality enhances analysis accuracy.