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Business Intelligence Interview Questions and Answers- Part 4
LISTEN TO THE BUSINESS INTELLIGENCE FAQs LIKE AN AUDIOBOOK
Are you preparing for a business intelligence interview that focuses on tools like Tableau, Power BI, or SQL? Then this interview guide is right for you. Employers today want candidates who not only know the theory behind BI but have deeper hands-on experience with the BI tools.
Whether you’re designing dashboards in Tableau or writing complex queries in SQL, you must be able to solve real-world business problems through data to shine in your interviews.
This collection of BI interview questions is specifically designed to test your practical knowledge of data visualization and analytics tools. You’ll face questions about data sources, relationships, measures vs. dimensions, calculated fields, and DAX functions.
So, whether you are preparing for a junior or experienced BI role, studying these questions will help you gain an edge during the interviews.
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Qualitative Data Analytics refers to a research method used to analyze non-numerical data to gain insights and understanding of human behavior, preferences, opinions, and experiences. Unlike quantitative data, which deals with measurable quantities and statistics, qualitative data focuses on the quality and characteristics of the data.
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The main difference between physical backups and logical backups lies in the way they capture and store data. Physical backups copy the raw binary data at the storage level, while logical backups capture data at a higher level, making it more flexible but potentially slower and less comprehensive. The choice between physical and logical backups depends on the specific backup and recovery requirements of the system.
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There are two distinct types of hierarchies: the Default Hierarchy, which comprises dimension objects arranged in the class’s original order of presentation, and the Custom Hierarchy, formed by customizing the Default Hierarchy.
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Dicing and Sliding” are two fundamental operations in the context of Business Intelligence and data analysis. These operations are used to manipulate data and extract useful insights from large datasets.
- Dicing: Dicing involves breaking down or partitioning a dataset into smaller subsets based on specific criteria or dimensions. It is similar to “slicing” a dataset, but instead of selecting a single dimension, you can select multiple dimensions to create a multi-dimensional subset of the data.
- Sliding: Sliding, also known as “sliding window” or “time-based sliding,” is an operation where you create moving subsets of data by selecting a continuous range of values based on a specific dimension (usually time). It is often used for trend analysis or identifying patterns over time.
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Data partitioning is a technique used in database management and distributed computing systems to divide a large dataset into smaller, more manageable subsets called partitions. Each partition contains a portion of the overall data, making it easier to store, process, and retrieve information efficiently.
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A repository refers to a centralized storage location or database where data related to BI activities is stored, managed, and organized. It acts as a data warehouse specifically designed to hold various types of BI artifacts and metadata, providing a structured and efficient way to store and retrieve data for reporting, analytics, and other BI processes.
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The model typically used for text retrieval in Business Intelligence (BI) is the Vector Space Model.
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The subset of a data warehouse is commonly referred to as a “data mart.”
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K-means partitioning is well-suited for separated clusters.
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A neural network is a type of artificial intelligence model inspired by the human brain’s neural structure. It is designed to recognize patterns and relationships in data and can be used for a variety of tasks, including image recognition, natural language processing, and prediction.
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There are several types of neural networks, such as:
- Feedforward Neural Networks: The simplest form of neural networks, where data flows in one direction, from input to output, without any feedback loops.
- Convolutional Neural Networks (CNN): Primarily used for image recognition and computer vision tasks. They use convolutional layers to automatically detect patterns and features in images.
- Recurrent Neural Networks (RNN): Suitable for sequential data, such as NLP, time series analysis, and speech recognition.
- Long Short-Term Memory Networks (LSTM): A specialized type of RNN designed to overcome the vanishing gradient problem and better capture long-term dependencies in sequences.
- Generative Adversarial Networks (GAN): A type of neural network architecture that consists of two networks, a generator, and a discriminator, which are trained together to generate realistic data, such as images or text.
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The role of a CRM (Customer Relationship Management) system is to manage and improve interactions with customers, prospects, and business partners throughout the entire customer lifecycle. It is a software tool that allows businesses to store, organize, and analyze customer data, helping them build and maintain strong customer relationships.
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The primary components within the designer interface consist of: a Table browser, facilitating the selection of tables and columns; the Structure pane, which represents the database structure of the universe; and finally, the Universe pane, encompassing the components intended for use in BI (Business Intelligence).
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The main objective of a Business Intelligence (BI) system is to gather, store, analyze, and present business data in a meaningful and actionable way to support decision-making processes. BI systems are designed to provide valuable insights into an organization’s performance, operations, and trends, allowing businesses to make informed decisions and gain a competitive edge in their respective industries.
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The key difference between BRD and SRS lies in their level of detail and target audience. The BRD provides an overview of business needs and objectives, while the SRS provides a comprehensive blueprint for developers to build the software based on the business requirements. These two documents complement each other in the business analysis process, ensuring that the project stays aligned with the business goals and is successfully executed.
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The BCG Matrix, also known as the Boston Consulting Group Matrix, is a strategic management tool used to analyze and evaluate a company’s portfolio of products or business units based on their market growth rate and relative market share.
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In Business Analysis, two commonly used types of diagrams are:
- Use Case Diagrams: They showcase the functional requirements of the system by representing different use cases and how actors interact with the system to achieve specific goals.
- Data Flow Diagrams (DFD): They represent the movement of data from one process to another and the data stores involved. DFDs help in understanding the data transformation and data flow through the system.
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Let’s check out the difference between Fish Model and V Model based on different factors, such as:
- Purpose: The Fish Model is used for problem-solving and root cause analysis, while the V Model is used for software development and testing.
- Application: The Fish Model can be used in various industries and domains to identify the root causes of issues, whereas the V Model is primarily used in software development and testing projects.
- Structure: The Fish Model takes the form of a diagram, showing the cause-and-effect relationship, while the V Model is represented as a graphical representation with development and testing phases forming a V-shaped pattern.
- Focus: The Fish Model focuses on the reasons behind a problem, while the V Model focuses on ensuring the quality and correctness of the software product.
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INVEST is an acronym that stands for Independent, Negotiable, Valuable, Estimable, Sized Appropriately, and Testable. It is a valuable tool that can aid project managers and technical teams in ensuring the delivery of high-quality products and services.
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The Pugh Matrix, also known as the Pugh Method or Pugh Concept Selection, is a decision-making tool used to evaluate and compare multiple alternatives based on a set of criteria.