Data Collection And Preprocessing - Practice Test

[#11] Which of the following is NOT a common data preprocessing technique?

(A) Data Cleaning

(B) Data Visualization

(C) Data Encoding

(D) Data Imputation

(E) Data Encoding

(F) Data Normalization

(G) Data Imputation

(H) Data Aggregation

Correct Answer

(B) Data Visualization
(H) Data Aggregation

[#12] In data preprocessing, what is the term for the process of converting text data into numerical values?

(A) Data Encoding

(B) Data Tokenization

(C) Data Parsing

(D) Data Transformation

Correct Answer

(A) Data Encoding

[#13] What is the purpose of feature engineering in data preprocessing?

(A) To make data more complex

(B) To extract meaningful features

(C) To perform data cleaning

(D) To visualize data

(E) To make data more complex

(F) To extract meaningful features

(G) To perform data cleaning

(H) To visualize data

Correct Answer

(B) To extract meaningful features
(F) To extract meaningful features

[#14] Which data collection method involves selecting a subset of individuals or elements from a larger population for study?

(A) Observational Studies

(B) Experiments

(C) Surveys

(D) Sampling

Correct Answer

(D) Sampling

[#15] In data preprocessing, what is the term for the process of handling missing data by filling in the missing values with estimates?

(A) Data Imputation

(B) Data Normalization

(C) Data Transformation

(D) Data Encoding

Correct Answer

(A) Data Imputation

Data Collection And Preprocessing - Study Mode

[#11] Which of the following is NOT a common data preprocessing technique?

Correct Answer

[#12] In data preprocessing, what is the term for the process of converting text data into numerical values?

Correct Answer

[#13] What is the purpose of feature engineering in data preprocessing?

Correct Answer

[#14] Which data collection method involves selecting a subset of individuals or elements from a larger population for study?

Correct Answer

[#15] In data preprocessing, what is the term for the process of handling missing data by filling in the missing values with estimates?

Correct Answer