Question Bank : Power BI

 

Question Bank

Unit : 01

1.     Explain the role of Power BI in modern business intelligence and compare the features of Power BI Desktop and Power BI Service.

2.     Discuss the importance of different visualization types in Power BI and explain with examples how charts like Bar, Line, Pie, Treemap, and Matrix help in decision-making.

3.     Describe the process of importing data from external sources such as Excel and CSV into Power BI. How to use a web URL to connect Google Drive datasets, directly connecting to websites

4.     Explain the importance of Power Query Editor in Power BI and discuss the steps involved in cleaning and preparing raw data for analysis.

5.     Discuss the various data transformation techniques available in Power BI such as Merge, Append, Replace, and Split Columns with suitable examples.

6.     Explain the concept of data modeling in Power BI. Discuss the role of relationships, keys in building effective data models.

7.     Define DAX in Power BI and explain the difference between calculated columns and measures with examples.

8. What is data analysis? What are the different types of data analytics? 

Unit : 02

1.     Explain the role of Conditional Columns, Custom Columns, and Parameters in Power Query with suitable examples.

2.     Discuss the importance of aggregation, iterators, and filters in DAX for performing advanced calculations in Power BI.

3.     Explain the concept of Time Intelligence in DAX and describe how YTD, QTD, and MTD calculations help in analyzing business performance.

4.      Discuss the role of DAX in analytics and how it transforms raw data into meaningful business insights across industries.

5.      Explain the concepts of Null and Alternative Hypotheses. Discuss how hypothesis testing can be demonstrated using Power BI.

Unit: 03

1.     Explain the role of KPI Cards, Gauges, Tooltips, and the Decomposition Tree in enhancing business dashboards in Power BI.

2.     Discuss the use of Drillthrough, Bookmarks, Selection Pane, and Smart Narratives in creating interactive and dynamic reports.

3.     Describe different types of slicers available in Power BI and explain how syncing slicers across pages improves user experience.

4.     Discuss how Python scripting can be used for advanced visualizations in Power BI and explain the importance of statistical charts.

5. What is a hypothesis? With reference to a Sales dataset containing Sales and Profit columns, how can we frame the Null Hypothesis (H₀) and Alternate Hypothesis (H₁) to test whether Sales have a significant impact on Profit?

Unit: 04

1.     Explain the role of Workspaces, Apps, and Report Sharing in Power BI Service. How do these features support collaboration?

2.     Describe the process of publishing reports from Power BI Desktop to the Service and discuss the benefits of cloud-based reporting.

3.     Discuss the use of data alerts, subscriptions, and Q&A visuals in enhancing interactivity and proactive decision-making in Power BI dashboards.

Unit: 05

1.     

      1. Explain the concept of Predictive Analytics in Power BI. How can regression outputs (CSV files) be used for forecasting business outcomes?

2   2.  Describe how advanced analytics techniques such as clustering, trendlines, and forecasting enhance insights in Power BI.

      3. Discuss the role of data storytelling, narrative visuals, and real-time dashboards in making Power BI reports more impactful and actionable.

       4. Explain the AI-powered Visuals/features (Smart Narrative, Analyze, Decomposition Tree, Q&A, Quick Insights, Key Influencer) of Power BI with detailed steps. 

       5. Explain the Calendar table in Power BI. Why does it need? Explain the detailed steps to create the calendar table. 

       6. Explain Clustering in Power BI
  7. Explain conditional formatting in Power BI. What is its use? Explain the steps followed to use it.

      8. Discuss how interactive dashboards in Power BI contribute to effective business intelligence. In your answer, explain how they aid in data visualization, storytelling, and informed decision-making, and describe the features that make dashboards user-friendly and insightful.

Case Study 1 – Finance Domain

A multinational bank manages thousands of customers with diverse financial profiles across different regions. They maintain large datasets that include customer income, loan types, repayment history, credit scores, and account activity. The bank is interested in leveraging advanced analytics through Power BI to improve transparency in customer performance and reduce risks in loan approvals.

However, the bank faces challenges such as rising loan defaults, delays in repayment, and difficulty in identifying customers with high credit risks. The current manual reporting system is slow, making it hard for managers to take proactive decisions. The leadership wants an interactive Power BI dashboard that can not only provide an overview of current loan trends but also predict potential defaults in advance to ensure financial stability.

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Tasks for Students

1. Write a problem statement for the given case in 2–3 lines.
2. Identify the type of analytics that should be performed (Descriptive, Diagnostic, Predictive, Prescriptive).
3. Frame a hypothesis related to customer loan defaults.
4. Define objectives that the analysis should achieve.
5. Propose a Power BI–based solution that addresses the problem.

Case Study 2 – Healthcare Domain

A hospital chain with multiple branches is struggling to manage patient data efficiently. They store large volumes of information related to admissions, treatments, test results, doctor availability, and bed occupancy. Hospital administrators want to use Power BI to convert this data into meaningful insights that can support both clinical and operational decisions.

The problems they face include frequent shortages of hospital beds, difficulty in predicting patient inflow, and lack of visibility into treatment outcomes. Patients often experience long waiting times, and management is unable to allocate resources effectively. By applying advanced analytics in Power BI, the hospital expects to identify key factors that affect patient stays, optimize staff scheduling, and improve overall patient care.

Tasks for Students

1.     Write a problem statement for the given case in 2–3 lines.

2.     Identify the type of analytics that should be performed.

3.     Frame a hypothesis related to hospital operations.

4.     Define the objectives of the analysis.

5.     Propose a Power BI–based solution for the problem.

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Case Study 3 – E-Commerce Domain

An online retail company serves millions of customers across different regions and product categories. They track customer demographics, purchase history, website activity, abandoned carts, and product returns. The company wants to apply predictive analytics in Power BI to gain deeper insights into consumer behavior and drive personalized marketing strategies.

Currently, the company faces problems such as high cart abandonment rates, unpredictable sales patterns, and growing product return percentages. They also find it difficult to identify their most profitable products and loyal customers. Management expects an advanced dashboard that will not only display sales performance but also forecast demand, recommend product bundles, and improve customer retention.

Tasks for Students

1.     Write a problem statement for the given case in 2–3 lines.

2.     Identify the type of analytics that should be performed.

3.     Frame a hypothesis related to customer behavior.

4.     Define the objectives of the analysis.

5.     Propose a Power BI–based solution for the problem.

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Case Study 4 – Social Media Domain

A digital marketing agency manages social media campaigns for several brands across platforms such as Facebook, Instagram, Twitter, and YouTube. They collect data related to likes, shares, comments, impressions, and click-through rates. The agency wants to use Power BI to analyze engagement trends and provide clients with clear insights into their online presence.

The main problems include difficulty in identifying which type of content drives maximum engagement, inconsistent campaign performance, and challenges in forecasting user interaction levels. Clients demand quick, data-driven recommendations for future campaigns to improve brand visibility. With advanced analytics in Power BI, the agency expects to optimize content strategies, track campaign ROI, and improve client satisfaction.

Tasks for Students

1.     Write a problem statement for the given case in 2–3 lines.

2.     Identify the type of analytics that should be performed.

3.     Frame a hypothesis related to social media engagement.

4.     Define the objectives of the analysis.

5.     Propose a Power BI–based solution for the problem.