Business Intelligence Interview Questions and Answers- Part 6

Answer:

Below are the different connectivity modes available in Power BI:

• Import: In this mode, Power BI imports the data from the data source into its internal memory. It creates a local data model, and any changes or transformations made to the data will only affect the imported dataset in Power BI. This mode is suitable for small to medium-sized datasets that don’t require real-time updates.
• DirectQuery: In DirectQuery mode, Power BI sends queries directly to the data source whenever a report or visualization is rendered. The data remains in the source system, and Power BI retrieves only the required data for visualization. This mode is ideal for large datasets and scenarios where real-time data is needed.
• Composite Models: Power BI Composite models enable you to have multiple DirectQuery connections combined with an imported dataset in a single Power BI report. This allows you to join data from different sources and leverage the benefits of both Import and DirectQuery modes in a single report.

Answer:

Filters are used to control the data displayed in visualizations and tables. They allow you to refine and focus the data shown in your report to present the most relevant information to your audience. Filters can be applied to different elements within your report, such as individual visuals, entire pages, or the entire report itself.

There are two main types of filters in Power BI Reports:

1. Visual-level filters: These filters are applied to specific visuals, and they affect only the data displayed within that particular visual.
2. Page-level and report-level filters: These filters can be applied to an entire page or the entire report, respectively. When applied, these filters impact all the visuals and tables on the respective page or the whole report.

Answer:

Here are some common types of visualizations you can create in Power BI:

1. Bar Chart: A bar chart displays data using rectangular bars of varying lengths. It’s suitable for comparing categorical data or showing trends over time.
2. Column Chart: Similar to a bar chart, a column chart uses vertical bars to represent data, often used when the axis labels are long or when comparing data with negative values.
3. Line Chart: A line chart connects data points with straight lines, making it ideal for displaying trends over time or continuous data.
4. Pie Chart: Pie charts show the proportion of individual parts to the whole, commonly used to represent percentages or parts of a whole.
5. Donut Chart: Similar to a pie chart, a donut chart displays data in rings, providing space in the middle for additional information.
6. Treemap: A treemap visualizes hierarchical data using nested rectangles, with the size and color representing different data attributes.

Answer:

OLAP (Online Analytical Processing) is used for complex querying and analysis of historical data, providing multidimensional views and aggregations. OLTP (Online Transaction Processing), on the other hand, is used for real-time transaction processing, like inserting, updating, or deleting data in databases.

Answer:

In such cases, I can optimize queries, use data aggregation techniques, or consider data sampling to reduce the dataset size while maintaining data integrity.

Answer:

Online Transaction Processing (OLTP) is a type of data processing that manages and supports the real-time, day-to-day transactional operations of a business or organization. It is designed to handle a large number of short, interactive transactions that involve inserting, updating, or deleting small amounts of data.

Answer:

ERP stands for Enterprise Resource Planning. It is a software system that integrates various business processes and functions across an organization into a single unified platform. The primary purpose of ERP is to streamline and automate business operations, facilitate data sharing and communication between different departments, and provide real-time insights to aid in decision-making.

Answer:

An Executive Information System (EIS) is a specialized type of management information system (MIS) designed to provide top-level executives and decision-makers with easy access to relevant, timely, and concise information to support their strategic decision-making processes. EIS is primarily focused on meeting the needs of executives who require a high-level overview of the organization’s performance and key performance indicators.

Answer:

Dimensional Modeling (DM) is a data modeling technique used in data warehousing and business intelligence (BI) systems. It provides a way to organize and structure data in a way that facilitates easy and efficient querying and analysis. The primary goal of dimensional modeling is to support decision-making processes by providing a simplified view of the data, making it easier for end-users to understand and navigate.

Answer:

Some of the primary multidimensional analysis methods in BusinessObjects are:

• Slice and Dice: This method allows users to focus on specific portions of data (slices) and drill down or drill up to view detailed or summarized data (dice) within a multidimensional dataset.
• Drill Down and Drill Up: Users can explore data hierarchies by drilling down to view more detailed data or drilling up to see aggregated data at higher levels of the hierarchy.
• Pivoting: This method involves reorganizing data to view it from different angles, helping users gain insights from different perspectives.
• Filtering: Users can apply filters to data to narrow down the information displayed and focus on specific subsets of data that are relevant to their analysis.
• Ranking and Sorting: This method allows users to rank data based on certain measures or sort data based on specific attributes.

Answer:

JAD stands for “Joint Application Development.” It is a software development methodology that involves collaboration between stakeholders, users, and development teams to gather requirements, define project scope, and create a solution through intensive workshops and interactive sessions.

Answer:

Business modeling refers to the process of creating representations of various aspects of a business in order to analyze its current state, make informed decisions, and plan for the future. It involves using different techniques, tools, and methodologies to capture and organize data related to the business’s operations, resources, finances, and strategies.

Answer:

MoSCoW is a prioritization technique used in project management and software development to classify requirements based on their importance. The name “MoSCoW” stands for the four categories into which requirements are categorized:

1. Must-Have (M): These are essential requirements without which the project or product cannot be considered successful.
2. Should-Have (S): They are considered as high-priority items that should be included if possible.
3. Could-Have (C): They represent additional features or functionalities that may enhance the product but can be postponed if resources or time constraints arise.
4. Won’t Have (W): These are requirements that have been deemed unnecessary or not feasible for the current project.

Answer:

The term “SWOT” stands for:

1. Strengths: These are the internal factors that give an advantage to an entity over others. Strengths could include expertise, unique resources, a strong brand, etc that sets the entity apart from its competitors.
2. Weaknesses: These are the internal factors that put an entity at a disadvantage such as lack of certain skills, limited resources, or others. It hinders the entity’s ability to achieve its goals.
3. Opportunities: These are the external factors that could be advantageous to the entity like market trends, changes in consumer behavior, new technologies, or other favorable external circumstances.
4. Threats: These are the external factors or situations that could negatively impact the entity’s performance, including competition, economic downturns, regulatory changes, or any other challenges that the entity might face.

Answer:

A subquery, also known as a nested query or inner query, is a query that is embedded within another main query. Subqueries are used to retrieve data that will be used as a part of the main query’s condition or result set.

Answer:

There are several types of subqueries commonly used in BI, such as:

• Scalar Subquery
• Correlated Subquery
• Non-Correlated Subquery
• Nested Subquery
• IN Subquery
• EXISTS Subquery
• ANY/ALL Subquery

Answer:

Here are the main differences between a Data Warehouse System and a Transactional System:

1. Purpose:
   • Data Warehouse System: It is designed to store, consolidate, and manage large volumes of historical data from various sources.
   • Transactional System: It is designed to handle real-time transaction processing and is optimized for capturing, storing, and updating individual transactions as they occur.
2. Data Structure:
   • The data in a data warehouse is organized into a dimensional model, often using a star or snowflake schema, to facilitate data analysis and reporting.
   • The data in a transactional system is typically structured in a normalized form to reduce data redundancy and maintain data integrity.
3. Data Volume and History:
   • Data Warehouse System stores large volumes of historical data over a long period of time, often spanning several years.
   • Transactional System focuses on managing current, real-time data and usually stores a limited history of recent transactions.
4. Query and Reporting Performance:
   • Data Warehouse System is optimized for complex analytical queries and reporting, which may involve aggregations, joins, and data transformations.
   • Transactional System is optimized for fast and efficient single-record queries and transactions. It aims to ensure data integrity and support the smooth execution of day-to-day business operations.
5. Data Latency:
   • Data updates in a data warehouse are usually not in real-time. It undergoes periodic data integration processes, such as ETL, to refresh the data from source systems.
   • In transactional system, data changes are immediately visible and available for retrieval in real-time.
6. Data Usage:
   • Data Warehouse System is primarily used by business analysts, data scientists, and decision-makers to perform complex data analysis and create reports for strategic planning.
   • Transactional System is used by front-line employees and operational staff to carry out day-to-day business tasks.

Answer:

The Snowflake Schema is a type of database schema used in data warehousing and relational database management systems. It is designed to organize and structure data in a way that facilitates efficient querying and reporting.

Let’s check out the difference between Snowflake Schema and Star Schema:

• Normalization: Snowflake Schema is more normalized, whereas Star Schema is more denormalized.
• Structure: Snowflake Schema forms a complex network of related tables, while Star Schema has a simpler star-like structure with one central fact table and multiple dimension tables.
• Query Performance: Star Schema typically offers better query performance due to fewer joins, while Snowflake Schema may require more complex joins, potentially impacting performance.
• Data Duplication: Snowflake Schema minimizes data redundancy by normalizing tables, while Star Schema may have some data duplication in dimension tables.

Answer:

A Ragged Hierarchy, also known as an Unbalanced Hierarchy or a Hierarchical Ragged Structure, refers to a type of data structure or organizational system in which the hierarchy or levels of elements do not have a uniform or consistent number of sub-elements. In other words, some levels may have more sub-elements than others, resulting in an irregular or uneven structure.

Answer:

• A dimension table contains descriptive attributes that provide context and details about the data in a data warehouse. Dimension tables are typically used for slicing and dicing data to gain insights. Each row in a dimension table represents a unique dimension member, and each column represents a specific attribute or characteristic of that member.

Key characteristics of a dimension table:

• Contains descriptive data.
• Usually has a primary key that uniquely identifies each dimension member.
• Smaller in size compared to fact tables.
• Used for filtering, grouping, and organizing data during analysis.
• A fact table contains quantitative data that represent the business metrics or facts in a data warehouse. Fact tables are used to store the data associated with the business processes or events and are linked to dimension tables through foreign keys.

Key characteristics of a fact table:

• Contains quantitative data (facts).
• Often has multiple foreign keys that relate to dimension tables.
• Larger in size compared to dimension tables due to the inclusion of detailed transactional data.
• Used for aggregating data and performing calculations during analysis.