How does a drill-down report differ from an executive summary report in business intelligence?
- Drill-down reports are used for historical data analysis, while executive summary reports are designed for real-time reporting.
- Drill-down reports provide detailed insights by allowing users to navigate through hierarchical levels of data, while executive summary reports offer a concise overview of key information.
- Executive summary reports are more interactive than drill-down reports.
- Executive summary reports focus on individual data points, while drill-down reports analyze trends and patterns across the entire dataset.
Drill-down reports enable users to delve deeper into data by navigating through different levels of detail, providing a more granular understanding. In contrast, executive summary reports offer a high-level overview without detailed exploration.
The process of using a large number of decision trees to improve prediction accuracy is known as _______.
- Ensemble Learning
- Logistic Regression
- Naive Bayes
- Support Vector Machine
The process of using a large number of decision trees to improve prediction accuracy is known as Ensemble Learning. It involves combining the predictions of multiple models to achieve better overall performance. Logistic Regression, Naive Bayes, and Support Vector Machine are different machine learning approaches.
What will be the output of print(8 // 3) in Python?
- 2
- 2
- 2.6667
- 3
The double forward slash // in Python represents integer division, which discards the remainder. Therefore, the output is 2.
In digital marketing, 'Click-Through _______' is a vital metric for assessing ad performance.
- Conversion
- Engagement
- Impression
- Rate
'Click-Through Rate' (CTR) is a crucial metric in digital marketing that measures the percentage of people who click on an ad after seeing it. It is calculated by dividing the number of clicks by the number of impressions. In this context, the blank should be filled with "Rate."
Which role is typically responsible for defining and enforcing data quality standards?
- Chief Information Officer (CIO)
- Data Analyst
- Data Steward
- Database Administrator
The role typically responsible for defining and enforcing data quality standards is the Data Steward. Data Stewards play a key role in ensuring that data is accurate, consistent, and meets the organization's quality requirements.
If you need to extract data from multiple tables based on a set of complex conditions, which SQL feature would you primarily use?
- GROUP BY
- HAVING
- JOIN
- UNION
In scenarios where data needs to be extracted from multiple tables based on complex conditions, the JOIN operation is commonly used in SQL. JOIN allows you to combine rows from two or more tables based on a related column between them.
In statistics, what does the median represent in a data set?
- The middle value in a sorted list
- The most frequently occurring value
- The range of values
- The sum of all values divided by the number of values
The median is the middle value in a sorted list. It is not affected by extreme values and provides a measure of central tendency.
What function would you use to combine text from two different cells into one cell?
- COMBINE
- CONCATENATE
- JOIN
- MERGE
The CONCATENATE function is used to combine text from two or more cells into a single cell in Excel. It allows you to concatenate, or join, the contents of different cells.
In the healthcare sector, which data mining method would be optimal for predicting patient readmission risks?
- Association Rule Mining
- Classification
- Clustering
- Regression
Classification is optimal for predicting patient readmission risks in healthcare. It involves categorizing patients into different classes, such as high or low risk, based on relevant features. Regression, Association Rule Mining, and Clustering are not as suitable for this specific predictive task.
In graph theory, what algorithm is used to find the minimum spanning tree for a connected weighted graph?
- Bellman-Ford Algorithm
- Dijkstra's Algorithm
- Kruskal's Algorithm
- Prim's Algorithm
Prim's Algorithm is used to find the minimum spanning tree for a connected weighted graph. It starts with an arbitrary node and greedily adds the shortest edge that connects a vertex in the tree to a vertex outside the tree until all vertices are included.