Intelligent Data Modeling Assessment Test

Explanation
Data is stored in MOLAP (Multidimensional Online Analytical Processing) using multidimensional databases. Unlike relational databases, which organize data in tables with rows and columns, multidimensional databases store data in a multidimensional structure. This allows for efficient and fast retrieval of data for analytical purposes. The multidimensional structure is designed to represent data in multiple dimensions, such as time, geography, and product, allowing for complex analysis and data slicing. Therefore, the use of multidimensional databases is the most suitable method for storing data in MOLAP.

Explanation
Data mining is the correct answer because it involves the process of extracting useful information or patterns from large sets of data collected from various sources. It involves techniques such as data cleaning, data integration, and data transformation to discover insights and make predictions. Data dredging refers to the practice of searching for patterns or relationships in data without a specific hypothesis, while data begging and data merging are not commonly used terms in the context of data extraction.

Explanation
Data purging refers to the process of permanently deleting data from a data warehouse. It involves removing unwanted or outdated data that is no longer needed for analysis or reporting purposes. This helps to optimize storage space and improve the performance of the data warehouse. By purging unnecessary data, organizations can ensure that only relevant and up-to-date information is retained, making it easier to retrieve and analyze data effectively.

Explanation
Data cubes store data in a summarized version. Data cubes are multidimensional structures that organize data into dimensions and measures. These dimensions represent different attributes or characteristics of the data, while measures represent the numerical values being analyzed. By summarizing the data, data cubes provide a compact and efficient way to store and analyze large amounts of data, allowing for faster query processing and data analysis.

Explanation
Gender is an example of discrete data because it can only take on a limited number of distinct values, such as male or female. Discrete data consists of separate, distinct categories or values that cannot be subdivided further. In this case, gender is a categorical variable with two distinct categories, making it a clear example of discrete data.

Explanation
The correct answer is MODEL. In pattern matching algorithms, a model is used to represent the pattern that needs to be matched. The model contains the characteristics or features of the pattern, which are then compared with the input data to find matches. The other options, OLAP, MOLAP, and Decision Tree, are not directly related to pattern matching algorithms and do not play a role in this process.

Explanation
Time series can be used to predict continuous values of data. A time series is a sequence of data points collected at regular intervals over time. It represents the behavior of a variable or phenomenon over time and can be used to make predictions based on patterns and trends observed in the historical data. By analyzing the past values, seasonality, and trends in a time series, predictive models can be built to forecast future values. Therefore, time series analysis is a suitable method for predicting continuous values of data.

Explanation
Naive Bayes Algorithm is used to generate mining models. This algorithm is a probabilistic classifier that applies Bayes' theorem with strong independence assumptions between the features. It is commonly used in machine learning for text classification, spam filtering, and sentiment analysis. By analyzing the relationships between variables, Naive Bayes can generate mining models that can be used to make predictions or classify new data based on the learned patterns from the training data.

Explanation
Cluster is the correct answer because it is a technique used in data analysis to group sets of data with similar characteristics. It helps in identifying patterns and relationships within the data by grouping similar data points together. This allows for easier analysis and understanding of the data, and can be used in various fields such as marketing, finance, and healthcare.

Explanation
The Association algorithm is used for recommendation engines based on market analysis. This algorithm analyzes patterns and associations between different items or products in a dataset to make recommendations. It identifies relationships between items that are frequently purchased together and uses this information to suggest similar or complementary items to customers. By analyzing transaction data and identifying item associations, the Association algorithm helps in making personalized recommendations to customers based on their purchasing behavior.