AKTU Artificial Intelligence For Engineering(KMC 101) Part 3
Q:1. The first usage of Data came in:
1.1640
2.1954
3.1946
4.1940
Solution- 3. 1946
Reason- The first English use of the word “data” is from the 1640s. The word “data” was first used to mean “transmissible and storable computer information” in 1946.
Q:2. DIKW:
1.Stands for Data, Information, Knowledge, Wisdom
2.In 1994 Nathan Shedroff presented the DIKW hierarchy in an information design context
3.In this context data is considered as symbols representing signals
4.All the above statements are correct
Solution- 1.Stands for Data, Information, Knowledge, Wisdom
Reason 1- Knowledge Pyramid, Wisdom Hierarchy and Information Hierarchy are some of the names referring to the popular representation of the relationships between data, information, knowledge and wisdom in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid.
Reason 2- Knowledge Pyramid, Wisdom Hierarchy and knowledge Hierarchy are a number of the names pertaining to the favored representation of the relationships between data, information, knowledge and wisdom within the Data, Information, Knowledge, Wisdom (DIKW) Pyramid.
Q:3. Classification data type which is not on the basis of measurement:
1.Ratio data
2.Ordinal data
3.Boolean data (True/False)
4.Interval data
Solution- 3. Boolean data (True/False)
Reason- There are four levels of data measurements in classification data type: Nominal, Ordinal, Interval, and Ratio.
Q:4. Not a case of Qualitative vs Quantitative data:
1.Category vs Number
2.Observed vs Measured
3.Smell vs Height
4.Volume vs Color
Solution- 4.Volume vs Color
Reason 1- Quantitative data can be counted, measured, and expressed using numbers. Qualitative data is descriptive and conceptual. Qualitative data can be categorized based on traits and characteristics.
Reason 2- Quantitative data are often counted, measured, and expressed using numbers. Qualitative data is descriptive and conceptual. Qualitative data are often categorized supported traits and characteristics.
Q:5. User driven approach is
1.Data Mining
2.Deep Learning
3.OLTP
4.Machine Learning
Solution- 3.OLTP
Reason 1- Current data warehouse development methods. can fall within three basic groups: data –driven, goal-driven and user–driven. Implementation strategies.
Reason 2- Current data warehouse development methods. can fall within three basic groups: data -driven, goal-driven and user-driven. Implementation strategies.
Q:6. Physical storage of data:
1.CD-ROM
2.Distributed database
3.Cloud storage
4.None of the above
Solution- 1.CD-ROM
Reason 1- Physical (non-electronic) data may be stored in a variety of forms including photographs, film, optical media (e.g. CDs & DVDs), magnetic media (e.g. audio and video tapes or computer storage devices), artworks, paper documents or computer printouts.
Reason 2- Physical (non-electronic) data could also be stored during a sort of forms including photographs, film, optical media (e.g. CDs & DVDs), magnetic media (e.g. audio and video tapes or memory devices), artworks, paper documents or computer printouts.
Q:7. Which of the following statement is true for Data Warehouse?
1.It is semi-structured and raw
2.It is less agile with fixed configuration
3.It is designed for low-cost storage
4.All the above
Solution- 2.It is less agile with fixed configuration
Reason 1- A data warehouse is a highly structured data bank, with a fixed configuration and little agility. Changing the structure isn’t too difficult, at least technically, but doing so is time consuming when you account for all the business processes that are already tied to the warehouse.
Reason 2- A data warehouse may be a highly structured data bank, with a hard and fast configuration and tiny agility. Changing the structure isn’t too difficult, a minimum of technically, but doing so is time consuming once you account for all the business processes that are already tied to the warehouse.
Q:8. Importance of data:
1.It helps to analyze and visualize the performance
2.It helps to recommend correct options to the customers
3.It helps to solve complex problems
4.All the above
Solution- 4.All the above
Reason- All of the above steps are true in case of data. It helps us to in all the cases.
Q:9. Choose an incorrect statement:
1.ETL stands for Extraction, Transformation, Loading into repository.
2.Data cleaning is very important in data preparation.
3.Removal of outliers and smoothing of data is required to prepare data for further processing.
4.Data needs to be normalize.
Solution- 4.Data needs to be normalize.
Reason 1- Similarly, the goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. For machine learning, every dataset does not require normalization. It is required only when features have different ranges.
Reason 2- Similarly, the goal of normalization is to vary the values of numeric columns within the dataset to a standard scale, without distorting differences within the ranges of values. For machine learning, every dataset doesn’t require normalization. it’s required only features have different ranges.
Q:10. Data visualization tools are:
1.Pie chart
2.Histogram
3.Scatter Plot
4.All the above
Solution- 4.All the above
Reason 1- Because data visualization tools are used to represent data in pictorial form. and all the option above are used to represent data in visual form.
Reason 2- Because data visualization tools are wont to represent data in pictorial form. and every one the choice above are wont to represent data in visual form.
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