For sequential pattern mining, the _______ algorithm is widely used to identify frequent sequences in data sets.
- Apriori
- DBSCAN
- FP-Growth
- K-Means
The FP-Growth algorithm is widely used for sequential pattern mining. It efficiently identifies frequent sequences in data sets by employing a tree structure to represent the relationships between sequential patterns.
For a dashboard handling large datasets, what strategy is crucial for maintaining performance and speed?
- Data Compression
- Data Duplication
- Data Normalization
- Indexing
Indexing is crucial for maintaining performance and speed in a dashboard handling large datasets. It allows for efficient data retrieval by creating a data structure that accelerates the retrieval of rows based on the values in one or more columns.
For recursive queries in SQL, the ________ keyword is often used.
- CONNECT BY
- HIERARCHY
- RECURSIVE
- WITH
The WITH keyword, also known as Common Table Expressions (CTE), is often used in SQL for handling recursive queries. It allows you to define temporary result sets that can be referenced within the context of the main query.
When designing a dashboard for an educational institution, what features should be included to track student performance and engagement effectively?
- Aesthetic background images
- Static tables of test scores
- Student progress timelines and achievement badges
- Word clouds of student feedback
Student progress timelines and achievement badges are effective features for tracking student performance and engagement in an educational dashboard. They provide a visual representation of progress and accomplishments, fostering motivation. Word clouds and static tables may not capture the dynamic nature of student engagement effectively, and aesthetic background images are more for decoration than analytical value.
To change the structure of a database table, the _______ SQL statement is used.
- ALTER
- CHANGE
- MODIFY
- UPDATE
The ALTER SQL statement is used to modify the structure of a database table. It can be used to add, delete, or modify columns, as well as change data types or constraints.
In a situation where data consistency is crucial, and you have multiple related update operations, how would you manage these operations in SQL?
- Apply triggers
- Use indexes
- Use transactions
- Utilize stored procedures
To ensure data consistency in situations involving multiple related update operations, transactions are used in SQL. Transactions allow you to group multiple SQL statements into a single, atomic operation, ensuring that all changes are applied or none at all.
In the context of data mining, how is 'ensemble learning' best described?
- A technique that combines predictions from multiple models to improve accuracy and robustness.
- Using algorithms specifically designed for mining ensemble datasets.
- Focusing on individual model predictions for better interpretability.
- Ignoring the diversity of models for simplicity.
Ensemble Learning involves combining predictions from multiple models to enhance overall accuracy and reliability. It leverages the strengths of different models and reduces the risk of relying on a single model's limitations. The other options do not accurately describe ensemble learning.
In data mining, a _______ model is used to represent complex relationships by mimicking the workings of the human brain.
- Clustering
- Decision Tree
- Neural Network
- Regression
In data mining, a Neural Network model is used to represent complex relationships by mimicking the workings of the human brain. This model is particularly effective in capturing intricate patterns and relationships within data.
For a business requiring real-time analytics from geographically dispersed data sources, which cloud architecture would be most effective?
- Edge Computing
- Hybrid Cloud
- Multi-Cloud
- Serverless Computing
Edge computing would be most effective in this scenario. It allows real-time analytics by processing data closer to the source, reducing latency, and is ideal for geographically dispersed data sources.
In the context of time series, _______ refers to a model used for forecasting when data shows evidence of non-stationarity.
- ARIMA
- Exponential Smoothing
- Nonlinear Model
- Stationary Model
ARIMA (AutoRegressive Integrated Moving Average) models are suitable for forecasting when time series data exhibit non-stationarity, meaning the statistical properties change over time. ARIMA models involve differencing the series to achieve stationarity.