Business Intelligence Interview Questions and Answers- Part 7

Answer:

SOA stands for “Service-Oriented Architecture.” It is an architectural style that defines a collection of services in a software system, where these services communicate with each other over a network. SOA modeling involves designing and describing these services and their interactions to create a flexible and scalable system.

Answer:

In R programming, logical vectors are a fundamental data type used to represent logical (Boolean) values. A logical vector is a one-dimensional array that contains either “TRUE” or “FALSE” values, which are used to express binary logic. Each element in the vector corresponds to a specific condition, and the vector can be of any length.

Answer:

Analytical reporting refers to the process of gathering, analyzing, and presenting data and information in a way that provides insights and supports decision-making within an organization. It involves using various tools and techniques to examine data sets, identify patterns, trends, and correlations, and convert raw data into meaningful and actionable information.

Answer:

Here are some common techniques used for requirement prioritization:

• MoSCoW Method
• Kano Model
• Analytic Hierarchy Process (AHP)
• Cost of Delay
• Value-Based Prioritization

Answer:

In Power BI, “responsive slicers” refer to a feature that enables slicers to automatically adjust their size and layout to fit the available space on a report page or visual. Slicers are a type of filtering control in Power BI used to filter data in visuals or report pages, allowing users to interactively slice and dice the data to focus on specific subsets of information.

Answer:

The working of Power BI can be broken down into several stages:

1. Data Source Connection: The first stage involves connecting to one or more data sources. Power BI supports a wide range of data sources, including Excel files, databases, cloud-based services, etc.
2. Data Transformation: After connecting to the data source, data often requires cleaning and transformation to make it suitable for analysis. Power BI offers a Power Query Editor where users can apply various data transformation operations, such as filtering, sorting, removing duplicates, and more.
3. Data Modeling: Power BI uses a tabular data model that allows users to create relationships between tables using primary and foreign keys.
4. Report Creation: Once the data is connected and transformed, users can create interactive reports using Power BI’s report canvas. The report canvas provides a drag-and-drop interface to add visualizations like tables, charts, maps, gauges, etc.
5. Data Analysis: With the report created, users can now explore and analyze the data. Power BI offers powerful analytical capabilities to help users spot trends, outliers, and patterns in the data.
6. Dashboard Creation: Dashboards in Power BI provide a consolidated view of important information from different reports. Users can pin visualizations and KPIs from multiple reports onto a single dashboard, providing a holistic view of the business’s performance.
7. Data Sharing and Collaboration: Power BI allows users to share reports and dashboards with others in their organization or external stakeholders. Collaborators can view, interact with, and even edit the reports if given appropriate permissions.
8. Data Refresh: Since Power BI reports are often connected to live data sources or data files that are frequently updated, it’s essential to schedule data refreshes. Data refresh ensures that the reports always display the most up-to-date information.

Answer:

Self-service BI refers to the ability of end-users to access and analyze data on their own without the need for extensive technical skills or assistance from IT professionals. Here are some key advantages:

1. Faster Decision Making: With self-service BI, end-users can access real-time data and generate reports or dashboards instantly. This enables faster decision-making processes as users don’t have to wait for data analysts or IT teams to create and deliver reports.
2. Empowerment of Business Users: Self-service BI empowers business users to explore and analyze data independently. They can customize reports, visualize data, and derive insights without relying on specialized knowledge, reducing the dependency on IT teams and enhancing productivity.
3. Flexibility and Customization: End-users can tailor their data analysis to suit their specific needs and preferences. They can create personalized reports, dashboards, and visualizations.
4. Reduced IT Burden: By enabling end-users to handle their BI needs, IT departments can focus on more strategic tasks and complex data management issues.
5. Enhanced Collaboration and Sharing: Self-service BI tools provide collaboration features that allow users to share insights, reports, and dashboards with colleagues, fostering a data-driven culture across the organization.
6. Improved Data Accuracy and Quality: Self-service BI tools often connect to centralized data sources, ensuring that users access the most up-to-date and accurate information. This reduces the chances of using outdated or incorrect data in decision-making.
7. Better Insights and Discovery: Business users have a deeper understanding of their data and domain knowledge. When they interact directly with data through self-service BI, they can discover patterns, correlations, and insights that may not be apparent to someone who isn’t as familiar with the specific business context.
8. Cost Savings: Self-service BI can be more cost-effective as it reduces the need for extensive training and support, making it accessible to a wider range of users.
9. Real-time Monitoring and Alerts: Self-service BI tools can provide real-time monitoring and automated alerts, allowing end-users to stay informed about critical changes in their data.

Answer:

Data normalization is a crucial technique used in databases and data analysis to standardize and organize data, providing several benefits that contribute to better data management, analysis, and overall efficiency.

Answer:

Here are some key benefits of data normalization:

1. Minimizes data redundancy: By organizing data into separate, related tables, normalization reduces data duplication. This helps save storage space and ensures that data is consistent, preventing inconsistencies and anomalies that can occur when redundant data is updated in one place but not in others.
2. Improves data integrity: Data normalization enforces the use of primary keys and foreign keys, ensuring referential integrity. This means that relationships between tables are maintained correctly, avoiding the risk of having orphaned or invalid data.
3. Reduces data update anomalies: In a normalized database, updates and modifications to data can be done in one place, eliminating the risk of updating data in one table while forgetting to update it elsewhere, which could lead to data inconsistency and inaccuracies.
4. Enhances data query performance: Normalized data structures allow for more efficient and optimized queries, as they typically involve smaller tables with well-defined relationships. This can lead to faster retrieval of relevant information.
5. Simplifies data maintenance: With normalized data, adding, modifying, or deleting records becomes more straightforward, as changes only need to be made in one location. This reduces the chance of errors and makes database maintenance less complex.
6. Enables flexibility and scalability: As data normalization ensures that data is stored in a modular, organized manner, it becomes easier to adapt and scale the database as needs change over time. This makes it more suitable for evolving business requirements.
7. Supports data consistency: With data normalization, data values are consistent across the database, ensuring that the same information is represented uniformly throughout the system.
8. Reduces data redundancy: By eliminating data duplication, data normalization minimizes the risk of inconsistencies and errors arising from having multiple copies of the same data.

Answer:

Data denormalization is a database optimization technique used to improve the performance of certain types of queries in relational databases. It involves deliberately introducing redundancy into a database by combining normalized data into fewer tables or columns. Normalization, on the other hand, is the process of organizing data in a database to minimize redundancy and dependency.

Answer:

Here are some of the benefits of data denormalization:

1. Improved Query Performance: Denormalization can lead to faster query execution times because it reduces the need for complex joins between tables. By storing redundant data in a denormalized form, queries can be simplified, resulting in quicker data retrieval.
2. Reduced Joins: Denormalization minimizes the number of joins, making queries more efficient.
3. Enhanced Read Operations: Denormalization is particularly useful for read-heavy workloads or reporting purposes, where read operations outnumber write operations. Since denormalized data is pre-joined and stored together, reading data becomes faster and less taxing on the database server.
4. Simplified Application Code: Denormalization can lead to simplified application code, as the complexity of dealing with multiple related tables and handling joins is reduced. This can make the codebase easier to understand, maintain, and optimize.
5. Fewer Indexes: With denormalization, the number of indexes needed may decrease, which can save storage space and improve overall database performance.
6. Better Aggregation Performance: Denormalization can significantly speed up aggregation queries, since the relevant data is already combined and available in a denormalized form.
7. Reduced Locking and Concurrency Issues: In highly concurrent systems, normalization can lead to locking and contention problems when multiple users access and update related data simultaneously. Denormalization can mitigate these issues, leading to better concurrency control.

Answer:

Global Filters, also known as Global Search or Global Query, are filters applied to search across multiple columns or fields simultaneously in a dataset or database. When you apply a global filter, the system searches for the specified criteria across all relevant columns or fields and displays the matching results. It helps to narrow down the dataset by focusing on specific patterns or values that are common across different columns.

The main differences between Global Filters and Column Filters are:

1. Scope:
   • Global Filters search across multiple columns or fields simultaneously.
   • Column Filters apply to individual columns, affecting only the data within those columns.
2. Purpose:
   • Global Filters are useful for broad searches, looking for patterns or values that span multiple columns.
   • Column Filters are used for focused searches within specific categories or attributes.

Answer:

Power Query is a data transformation and data preparation tool developed by Microsoft. It is part of the Microsoft Power BI and Excel platforms, and it is designed to help users extract, transform, and load (ETL) data from various sources into a structured format for analysis, reporting, and visualization.

Answer:

Query parameters, also known as URL parameters or query strings, are key-value pairs that are appended to a URL to provide additional information to a web server when making an HTTP request. They are commonly used in web development to pass data from the client-side to the server-side to customize the response or perform specific actions.

Answer:

DAX is a formula language used in Power BI, Power Pivot, and Analysis Services to create custom calculations and expressions in data models.

Answer:

Using variables in DAX can bring several advantages, including:

1. Readability and Maintainability: Variables allow you to store intermediate results or complex expressions with meaningful names. It makes the DAX code more readable and easier to understand.
2. Code Reusability: By using variables, you can define expressions once and reuse them multiple times within a DAX formula.
3. Performance Optimization: DAX formulas can sometimes involve calculations that are resource-intensive or repetitive. By using variables to store intermediate results, you can avoid recalculating the same expressions multiple times.
4. Debugging and Troubleshooting: When troubleshooting DAX formulas, variables can be instrumental in inspecting intermediate results at different stages of the calculation.
5. Clarity in Complex Expressions: In DAX, expressions can become complex, especially when dealing with nested functions. Using variables to break down the expressions into smaller, manageable parts can enhance clarity and reduce the chance of errors.
6. Avoiding Recursion Issues: DAX doesn’t support recursive functions. However, by using variables to store iterative results, you can achieve similar results without encountering recursion-related problems.

Answer:

A calculated column is a feature commonly found in relational databases or spreadsheet applications that allows users to create new columns in a table based on the values of other existing columns. These new columns are derived through the application of predefined formulas or expressions, which use the values from one or more existing columns to calculate the values of the new column.

Answer:

Query collapsing, also known as session-based query reformation, is a technique used in information retrieval systems, particularly in web search engines, to improve the search results for users who issue a sequence of related queries during a single search session.

Answer:

In Power BI, a bookmark is a feature that allows you to capture the current state of a report, including the filters, slicers, and visualizations that are currently applied. It essentially saves the configuration of the report so that you can return to that specific view later.

Answer:

Handling many-to-many relationships in Power BI involves setting up proper data modeling, using cross-filtering option and intermediate tables to bridge the relationships between tables.