Students find these Class 9 AI MCQ Chapter 2 Data Literacy Class 9 MCQ Online Test with Answers helpful for self-assessment and preparation.
Class 9 AI Data Literacy MCQ
MCQ on Data Literacy Class 9
Class 9 Data Literacy MCQ – Data Literacy MCQ Class 9
Multiple Choice Questions
Question 1.
Problem scoping, data acquisition, data exploration are the stages of _____.
(a) Project cycle
(b) Problem scoping
(c) Domains of AI
(d) None of the above
Answer:
(a) Project cycle
Question 2.
Which of the following comes in problem scoping ?
(a) Identifying the problem
(b) Setting of the goals
(c) Both a and b
(d) None of these
Answer:
(c) Both a and b
Question 3.
What are the 4 Ws ?
(a) How, when, why and what
(b) Word, why, well and work
(c) Who, what, where and why
(d) All of the above
Answer:
(c) Who, what, where and why
Question 4.
Problem scoping compromises which of the following?
(a) Context
(b) Need
(c) Vision
(d) All of the above
Answer:
(d) All of the above
Question 5.
Which is the factor affecting problem scoping?
(a) Finance
(b) Physical resources
(c) Company
(d) Society
Answer:
(a) Finance
Question 6.
How many components are there in system map?
(a) 5
(b) 2
(c) 6
(d) 3
Answer:
(d) 3
Question 7.
Which of the following elements do not define water cycle system?
(a) Clouds
(b) Snow
(c) Rivers
(d) Sky
Answer:
(d) Sky
Question 8.
What are the two types of basic level data?
(a) Textual data and unstructured data
(b) Numeric data and discrete data
(c) Textual and numeric data
(d) Mechanic data and big data
Answer:
(c) Textual and numeric data
Question 9.
How many types of other data are there?
(a) Time stamped data
(b) Big data
(c) Open data
(d) All of the above
Answer:
(d) All of the above
Question 10.
Which of the following is not a parameter of big data?
(a) Volume
(b) Mass
(c) Variety
(d) Velocity
Answer:
(b) Mass
Question 11.
Discrete data and continuous data are part of
(a) Textual data
(b) Numeric data
(c) Both a and b
(d) All of the above
Answer:
(b) Numeric data
Question 12.
Cultivating Data Literacy means:
(a) Utilize vocabulary and analytical skills
(b) Acquire, develop, and improve data literacy skills
(c) Develop skills in statistical methodologies
(d) Develop skills in Math
Answer:
(b) Acquire, develop, and improve data literacy skills
Question 13.
Which of the following are parts of decision tree?
(a) Root node
(b) Branching
(c) Decision node
(d) All of them
Answer:
(d) All of them
Question 14.
For comparing smaller data sets, – –is/are used.
(a) Bar chart
(b) Line chart
(c) Pie chart
(d) Both (a) & (b)
Answer:
(d) Both (a) & (b)
Question 15.
The word used to refer to both machine learning and deep learning is _____.
(a) AI
(b) Data visualization
(c) Visual representation
(d) None of the above
Answer:
(a) AI
Question 16.
The main idea behind modelling is ________
(a) Writing data
(b) Writing codes
(c) Building block
(d) All of the above
Answer:
(b) Writing codes
Question 17.
A decision tree is comparable to a _____.
(a) Arrow
(b) Flow chart
(c) Branch
(d) Line
Answer:
(b) Flow chart
Question 18.
Every phase of a project cycle ends with ________.
(a) Final evaluation
(b) Regular evaluation
(c) Correction process
(d) None of the above
Answer:
(b) Regular evaluation
Question 19.
Using a project for its intended purpose on-site is referred to as _____.
(a) Deployment
(b) Evaluation
(c) Project cycle
(d) Revaluation
Answer:
(a) Deployment
Question 20.
Phase of evaluation involves ______.
(a) Using the system in a controlled environment
(b) Identifying areas for improvement
(c) Identifying future research areas
(d) All of the above
Answer:
(d) All of the above
Question 21.
Which of the following is not included in the canvas of the 4 Ws problem?
(a) Who?
(b) Why?
(c) What?
(d)Which?
Answer:
(d)Which?
Question 22.
Which of the following is a problem scoping technique?
(a) System mapping
(b) 4 W canvas
(c) Data features
(d) Web scraping
Answer:
(b) 4 W canvas
Question 23.
________ does not fall under the project cycle’s review and deployment stage.
(a) Project testing
(b) System modification
(c) Project review
(d) Project modelling
Answer:
(d) Project modelling
Question 24.
Leaf nodes in a decision tree represent ________
(a) Conclusion
(b) Decision
(c) Condition
(d) Branch
Answer:
(a) Conclusion
Question 25.
The main purpose to work with data are- ________
(a) Comparing values
(b) Establishing relationships
(c) Distribution and composition
(d) All of the above
Answer:
(d) All of the above
Question 26.
Which one of the following is the second stage of AI project cycle?
a. Data Exploration
b. Data Acquisition
c. Modelling
d. Problem Scoping
Answer:
b. Data Acquisition
Question 27.
Which of the following comes under Problem Scoping?
a. System Mapping
b. 4 Ws Canvas
c. Data Features
d. Web scraping
Answer:
b. 4 Ws Canvas
Question 28.
Which of the following is not valid for Data Acquisition?
a. Web scraping
b. Surveys
c. Sensors
d. Announcements
Answer:
d. Announcements
Question 29.
If an arrow goes from X to Y with a – (minus) sign, it means that
a. If X increases, Y decreases
b. The direction of relation is opposite
c. If X increases, Y increases
d. It is a bi-directional relationship
Answer:
a. If X increases, Y decreases
Question 30.
Which of the following is not a part of the 4 Ws Problem Canvas?
a. Who?
b. Why?
c. What?
d. Which?
Answer:
d. Which?
Question 31.
The _____ provides guidance on using data efficiently and with all levels of awareness.
(a) data security framework
(b) data literacy framework
(c) data privacy framework
(d) data acquisition framework
Answer:
(b) data literacy framework
Question 32.
______ allows us to understand why things are happening in a particular way.
(a) data
(b) information
(c) knowledge
(d) wisdom
Answer:
(c) knowledge
Question 33.
_____ is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.
(a) data security
(b) data literacy
(c) data privacy
(d) data acquisition
Answer:
(a) data security
Question 34.
What are the basic building blocks of qualitative data?
(a) Individuals
(b) Units
(c) Categories
(d) Measurements
Answer:
(c) Categories
Question 35.
Which among these is not a type of data interpretation?
(a) Textual
(b) Tabular
(c) Graphical
(d) Raw data
Answer:
(d) Raw data
Question 36.
A Bar Graph is an example of?
(a) Textual
(b) Tabular
(c) Graphical
(d) None of the above
Answer:
(c) Graphical
Question 37.
______ relates to the manipulation of data to produce meaningful insights.
(a) Data processing
(b) Data interpretation
(c) Data Anąlysis
(d) Data presentation
Answer:
(c) Data Anąlysis
Assertion-Reasoning Based Questions
Study the two statements labeled as assertion (a) and reason (r). Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true
Question 1.
Assertion (A) : Data literacy is essential for making informed business decisions.
Reason (R) Data literacy enables individuals to understand and analyze data, leading to more accurate and effective decision-making.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a
Question 2.
Assertion (A) : Data visualization tools are unnecessary for data literacy.
Reason (R) : Data literacy focuses solely on the ability to interpret raw data and statistics;
Answer:
(c) If a is true but r is false
Question 3.
Assertion (A) : Teaching data literacy skills in schools can enhance students’ critical thinking abilities.
Reason (R) : Data literacy involves analyzing data sets, recognizing patterns, and making evidence-based conclusions.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a
Question 4.
Assertion (A) : Data literacy is only important for data scientists and analysts.
Reason (R ) : Only professionals working directly with data need to understand how to interpret and use data effectively.
Answer:
(d) If a is false but r is true
Question 5.
Assertion (A) : Misinterpretation of data can lead to poor decision-making.
Reason (R) : Data literacy helps individuals avoid common pitfalls such as correlation versus causation errors and misrepresentation of statistical significance.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a