GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model
What’s inside:
🔘 A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘 GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘 Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘 Two MTP heads, enabling up to 2.2x faster generation;
🔘 FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘 A new online RL stage after SFT and DPO.
Results:
🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
➡️ HuggingFace
The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.
What’s inside:
Results:
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.
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Scenario based Interview Questions & Answers for Data Analyst
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
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1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :)
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𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Q: Find the customer(s) who placed orders in every month of the year 2025.
Assume the table structure:
orders(order_id, customer_id, order_date)
𝗠𝗲: Challenge accepted! 💪
💡 Explanation:
This query identifies customers who placed at least one order in every month of 2025.
•
•
•
•
This question tests your understanding of:
✅ Date Functions (YEAR, MONTH)
✅ GROUP BY
✅ HAVING
✅ COUNT(DISTINCT)
🎯 Expected Output Example
| Customer ID |
|-------------|
| 101 |
| 205 |
These customers placed at least one order in every month of 2025.
🚀 Alternative (Database-Agnostic SQL)
This version works with databases like PostgreSQL and Oracle that support the
🚀 Tip for SQL Job Seekers:
Whenever you see interview questions containing phrases like:
"Every month" / "Every quarter" / "Every year" / "Every category"
Think of
❤️ React with ❤️ for more interview challenges!
You have 2 minutes to solve this SQL query.
Q: Find the customer(s) who placed orders in every month of the year 2025.
Assume the table structure:
orders(order_id, customer_id, order_date)
𝗠𝗲: Challenge accepted! 💪
SELECT
customer_id
FROM orders
WHERE YEAR(order_date) = 2025
GROUP BY customer_id
HAVING COUNT(DISTINCT MONTH(order_date)) = 12;
💡 Explanation:
This query identifies customers who placed at least one order in every month of 2025.
•
WHERE YEAR(order_date) = 2025 filters orders from the year 2025•
GROUP BY customer_id groups all orders by customer•
COUNT(DISTINCT MONTH(order_date)) counts the unique months in which each customer placed an order•
HAVING ... = 12 ensures the customer has orders in all 12 monthsThis question tests your understanding of:
✅ Date Functions (YEAR, MONTH)
✅ GROUP BY
✅ HAVING
✅ COUNT(DISTINCT)
🎯 Expected Output Example
| Customer ID |
|-------------|
| 101 |
| 205 |
These customers placed at least one order in every month of 2025.
🚀 Alternative (Database-Agnostic SQL)
SELECT
customer_id
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2025
GROUP BY customer_id
HAVING COUNT(DISTINCT EXTRACT(MONTH FROM order_date)) = 12;
This version works with databases like PostgreSQL and Oracle that support the
EXTRACT() function.🚀 Tip for SQL Job Seekers:
Whenever you see interview questions containing phrases like:
"Every month" / "Every quarter" / "Every year" / "Every category"
Think of
COUNT(DISTINCT ...) combined with GROUP BY and HAVING. This is a very common SQL interview pattern.❤️ React with ❤️ for more interview challenges!
❤15
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Offers a wide range of free learning resources through Microsoft Learn, helping students, freshers, and professionals build job-ready skills at their own pace.
✅ 100% FREE self-paced learning modules
✅ Official learning platform from Microsoft
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𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Q: Find the employee(s) who received the highest salary increment compared to their previous salary.
Assume the table structure:
salary_history(employee_id, salary, effective_date)
𝗠𝗲: Challenge accepted! 💪
💡 Explanation:
This query calculates each employee's salary increment and then finds the highest increment across all employees.
• LAG(salary) retrieves the employee's previous salary
• The difference between the current and previous salary gives the increment
• DENSE_RANK() ranks increments from highest to lowest
• The outer query returns all employees tied for the highest salary increment
This question tests your understanding of:
✅ LAG() Window Function
✅ Common Table Expressions (CTEs)
✅ DENSE_RANK()
✅ Time-Series Data Analysis
🎯 Expected Output Example
Employee ID | Salary Increment
101 | 20,000
205 | 20,000
Both employees received the largest salary increase.
🚀 Why Interviewers Ask This?
This is a classic window function interview question. It evaluates your ability to compare a row with its previous row—a common requirement in payroll, finance, and audit systems.
🚀 Tip for SQL Job Seekers:
Master these analytical window functions:
LAG() / LEAD() / FIRST_VALUE() / LAST_VALUE() / NTILE()
These functions are frequently tested in product-based companies and data-focused interviews because they simplify complex row-by-row comparisons.
❤️ React with ❤️ for more interview challenges!
You have 2 minutes to solve this SQL query.
Q: Find the employee(s) who received the highest salary increment compared to their previous salary.
Assume the table structure:
salary_history(employee_id, salary, effective_date)
𝗠𝗲: Challenge accepted! 💪
WITH salary_changes AS (
SELECT
employee_id,
salary,
effective_date,
salary - LAG(salary) OVER (
PARTITION BY employee_id
ORDER BY effective_date
) AS salary_increment
FROM salary_history
)
SELECT
employee_id,
salary_increment
FROM (
SELECT
employee_id,
salary_increment,
DENSE_RANK() OVER (
ORDER BY salary_increment DESC
) AS rnk
FROM salary_changes
WHERE salary_increment IS NOT NULL
) ranked
WHERE rnk = 1;
💡 Explanation:
This query calculates each employee's salary increment and then finds the highest increment across all employees.
• LAG(salary) retrieves the employee's previous salary
• The difference between the current and previous salary gives the increment
• DENSE_RANK() ranks increments from highest to lowest
• The outer query returns all employees tied for the highest salary increment
This question tests your understanding of:
✅ LAG() Window Function
✅ Common Table Expressions (CTEs)
✅ DENSE_RANK()
✅ Time-Series Data Analysis
🎯 Expected Output Example
Employee ID | Salary Increment
101 | 20,000
205 | 20,000
Both employees received the largest salary increase.
🚀 Why Interviewers Ask This?
This is a classic window function interview question. It evaluates your ability to compare a row with its previous row—a common requirement in payroll, finance, and audit systems.
🚀 Tip for SQL Job Seekers:
Master these analytical window functions:
LAG() / LEAD() / FIRST_VALUE() / LAST_VALUE() / NTILE()
These functions are frequently tested in product-based companies and data-focused interviews because they simplify complex row-by-row comparisons.
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📈 Job seekers trying to improve employability
🚀 Anyone who wants to build a future-proof career with better salary potential
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🚀 Essential Tools Every Data Analyst Should Know
If you're starting your journey as a Data Analyst, focus on these essential tools first. These are the tools most commonly required in job descriptions and used in day-to-day work.
📊 1. Microsoft Excel
Used For:
Data Cleaning
Formulas & Functions
Pivot Tables
Dashboards
🗄️ 2. SQL
Used For:
Querying Databases
Data Extraction
Data Analysis
Reporting
📈 3. Power BI
Used For:
Interactive Dashboards
Data Visualization
Business Intelligence
KPI Reporting
📊 4. Tableau
Used For:
Data Visualization
Dashboard Creation
Business Reporting
🐍 5. Python
Used For:
Data Cleaning
Automation
Data Analysis
Data Visualization
🔄 6. Power Query
Used For:
Data Transformation
Data Cleaning
ETL Processes
🚀 Double Tap ❤️ For More
If you're starting your journey as a Data Analyst, focus on these essential tools first. These are the tools most commonly required in job descriptions and used in day-to-day work.
📊 1. Microsoft Excel
Used For:
Data Cleaning
Formulas & Functions
Pivot Tables
Dashboards
🗄️ 2. SQL
Used For:
Querying Databases
Data Extraction
Data Analysis
Reporting
📈 3. Power BI
Used For:
Interactive Dashboards
Data Visualization
Business Intelligence
KPI Reporting
📊 4. Tableau
Used For:
Data Visualization
Dashboard Creation
Business Reporting
🐍 5. Python
Used For:
Data Cleaning
Automation
Data Analysis
Data Visualization
🔄 6. Power Query
Used For:
Data Transformation
Data Cleaning
ETL Processes
🚀 Double Tap ❤️ For More
❤23
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𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Find the employee(s) who have worked on the highest number of distinct projects.
Assume the table structure: employee_projects(employee_id, project_id)
𝗠𝗲: Challenge accepted! 💪
💡 Explanation:
This query counts the number of unique projects each employee has worked on and identifies those with the highest count.
• COUNT(DISTINCT project_id) counts unique projects for each employee
• GROUP BY employee_id creates one record per employee
• DENSE_RANK() ranks employees based on the number of projects
• The outer query returns all employees tied for the highest number of projects
This question tests your understanding of:
✅ COUNT(DISTINCT)
✅ GROUP BY
✅ Window Functions DENSE_RANK
✅ Ranking Aggregated Results
🎯 Expected Output Example
Employee ID | Total Projects
101 | 12
205 | 12
Both employees have worked on the highest number of distinct projects.
🚀 Alternative Without Window Functions
This solution uses nested subqueries and MAX() instead of window functions.
🚀 Tip for SQL Job Seekers:
Many interview questions involve ranking aggregated results, such as:
Highest number of projects, Most orders, Maximum sales, Highest attendance, Most logins
Practice combining GROUP BY with window functions like DENSE_RANK() to solve these efficiently.
❤️ React with ❤️ for more interview challenges!
You have 2 minutes to solve this SQL query.
Find the employee(s) who have worked on the highest number of distinct projects.
Assume the table structure: employee_projects(employee_id, project_id)
𝗠𝗲: Challenge accepted! 💪
SELECT
employee_id,
total_projects
FROM (
SELECT
employee_id,
COUNT(DISTINCT project_id) AS total_projects,
DENSE_RANK() OVER (
ORDER BY COUNT(DISTINCT project_id) DESC
) AS rnk
FROM employee_projects
GROUP BY employee_id
) ranked
WHERE rnk = 1;
💡 Explanation:
This query counts the number of unique projects each employee has worked on and identifies those with the highest count.
• COUNT(DISTINCT project_id) counts unique projects for each employee
• GROUP BY employee_id creates one record per employee
• DENSE_RANK() ranks employees based on the number of projects
• The outer query returns all employees tied for the highest number of projects
This question tests your understanding of:
✅ COUNT(DISTINCT)
✅ GROUP BY
✅ Window Functions DENSE_RANK
✅ Ranking Aggregated Results
🎯 Expected Output Example
Employee ID | Total Projects
101 | 12
205 | 12
Both employees have worked on the highest number of distinct projects.
🚀 Alternative Without Window Functions
SELECT
employee_id,
COUNT(DISTINCT project_id) AS total_projects
FROM employee_projects
GROUP BY employee_id
HAVING COUNT(DISTINCT project_id) = (
SELECT MAX(project_count)
FROM (
SELECT
COUNT(DISTINCT project_id) AS project_count
FROM employee_projects
GROUP BY employee_id
) t
);
This solution uses nested subqueries and MAX() instead of window functions.
🚀 Tip for SQL Job Seekers:
Many interview questions involve ranking aggregated results, such as:
Highest number of projects, Most orders, Maximum sales, Highest attendance, Most logins
Practice combining GROUP BY with window functions like DENSE_RANK() to solve these efficiently.
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🚀 Power BI Interview Challenge #1 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
Order Date
Sales
Create a DAX measure to calculate Year-to-Date (YTD) Sales.
𝗠𝗲: Challenge accepted! 💪
YTD Sales =
TOTALYTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
TOTALYTD() calculates the cumulative sales from the beginning of the year up to the current date.
• SUM(Sales) returns the total sales amount
• Sales[Order Date] is the date column used for the YTD calculation
• The measure automatically resets at the start of each new year[Sales]
🎯 Expected Output Example
Month | Sales | YTD Sales
--- | --- | ---
Jan | 10,000 | 10,000
Feb | 15,000 | 25,000
Mar | 12,000 | 37,000
Apr | 18,000 | 55,000
🚀 Bonus (Using a Calendar Table)
YTD Sales =
TOTALYTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar/Date table is considered a Power BI best practice and is recommended for all time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Time Intelligence is one of the most frequently tested topics in Power BI interviews. Make sure you can confidently write measures for:
• YTD (Year-to-Date)
• MTD (Month-to-Date)
• QTD (Quarter-to-Date)
• Previous Year Sales
• YoY Growth %
• Rolling 12 Months
These are commonly used in business dashboards and technical interviews.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
Order Date
Sales
Create a DAX measure to calculate Year-to-Date (YTD) Sales.
𝗠𝗲: Challenge accepted! 💪
YTD Sales =
TOTALYTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
TOTALYTD() calculates the cumulative sales from the beginning of the year up to the current date.
• SUM(Sales) returns the total sales amount
• Sales[Order Date] is the date column used for the YTD calculation
• The measure automatically resets at the start of each new year[Sales]
🎯 Expected Output Example
Month | Sales | YTD Sales
--- | --- | ---
Jan | 10,000 | 10,000
Feb | 15,000 | 25,000
Mar | 12,000 | 37,000
Apr | 18,000 | 55,000
🚀 Bonus (Using a Calendar Table)
YTD Sales =
TOTALYTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar/Date table is considered a Power BI best practice and is recommended for all time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Time Intelligence is one of the most frequently tested topics in Power BI interviews. Make sure you can confidently write measures for:
• YTD (Year-to-Date)
• MTD (Month-to-Date)
• QTD (Quarter-to-Date)
• Previous Year Sales
• YoY Growth %
• Rolling 12 Months
These are commonly used in business dashboards and technical interviews.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
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🚀 Power BI Interview Challenge #2 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the columns: Order Date & Sales
Create a DAX measure to calculate Month-to-Date (MTD) Sales.
𝗠𝗲: Challenge accepted! 💪
MTD Sales =
TOTALMTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
• TOTALMTD() calculates cumulative sales from the beginning of the current month up to the selected date.
• SUM(Sales) returns the total sales amount.
• Sales[Order Date] is the date column used for the MTD calculation.
• The measure automatically resets at the beginning of each new month.
🎯 Expected Output Example
Date | Sales | MTD Sales
Jul 1 | 2,000 | 2,000
Jul 2 | 3,500 | 5,500
Jul 3 | 1,500 | 7,000
Jul 4 | 4,000 | 11,000
🚀 Bonus (Using a Calendar Table)
MTD Sales =
TOTALMTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar table improves model performance and ensures accurate time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Always create a proper Date Table and mark it as a Date Table in Power BI before using Time Intelligence functions. Many interview questions are designed to test this best practice.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the columns: Order Date & Sales
Create a DAX measure to calculate Month-to-Date (MTD) Sales.
𝗠𝗲: Challenge accepted! 💪
MTD Sales =
TOTALMTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
• TOTALMTD() calculates cumulative sales from the beginning of the current month up to the selected date.
• SUM(Sales) returns the total sales amount.
• Sales[Order Date] is the date column used for the MTD calculation.
• The measure automatically resets at the beginning of each new month.
🎯 Expected Output Example
Date | Sales | MTD Sales
Jul 1 | 2,000 | 2,000
Jul 2 | 3,500 | 5,500
Jul 3 | 1,500 | 7,000
Jul 4 | 4,000 | 11,000
🚀 Bonus (Using a Calendar Table)
MTD Sales =
TOTALMTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar table improves model performance and ensures accurate time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Always create a proper Date Table and mark it as a Date Table in Power BI before using Time Intelligence functions. Many interview questions are designed to test this best practice.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
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🚀 Power BI Interview Challenge #3 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
• Order Date
• Sales
Create a DAX measure to calculate Year-over-Year (YoY) Sales Growth %.
𝗠𝗲: Challenge accepted! 💪
💡 Explanation:
This measure calculates the percentage growth in sales compared to the same period in the previous year.
•
•
•
This challenge tests your understanding of:
✅ Variables (VAR)
✅ CALCULATE()
✅ SAMEPERIODLASTYEAR()
✅ DIVIDE()
✅ Time Intelligence
🎯 Expected Output Example
For Year 2025: Sales = 120,000, Previous Year Sales = 100,000, YoY Growth % = 20%
For Year 2026: Sales = 150,000, Previous Year Sales = 120,000, YoY Growth % = 25%
🚀 Bonus (YoY Sales Difference)
This measure returns the absolute increase or decrease in sales compared to the previous year.
🚀 Tip for Power BI Job Seekers:
CALCULATE() is the most important DAX function. Learn how it modifies the filter context because it's used in almost every advanced Power BI interview question.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
• Order Date
• Sales
Create a DAX measure to calculate Year-over-Year (YoY) Sales Growth %.
𝗠𝗲: Challenge accepted! 💪
YoY Growth % =
VAR CurrentYearSales = [Total Sales]
VAR PreviousYearSales =
CALCULATE(
[Total Sales],
SAMEPERIODLASTYEAR('Calendar'[Date])
)
RETURN
DIVIDE(
CurrentYearSales - PreviousYearSales,
PreviousYearSales,
0
)
💡 Explanation:
This measure calculates the percentage growth in sales compared to the same period in the previous year.
•
CurrentYearSales stores the current period's sales.•
SAMEPERIODLASTYEAR() retrieves sales for the same period last year.•
DIVIDE() safely calculates the percentage growth and avoids divide-by-zero errors.This challenge tests your understanding of:
✅ Variables (VAR)
✅ CALCULATE()
✅ SAMEPERIODLASTYEAR()
✅ DIVIDE()
✅ Time Intelligence
🎯 Expected Output Example
For Year 2025: Sales = 120,000, Previous Year Sales = 100,000, YoY Growth % = 20%
For Year 2026: Sales = 150,000, Previous Year Sales = 120,000, YoY Growth % = 25%
🚀 Bonus (YoY Sales Difference)
YoY Sales Difference =
[Total Sales] -
CALCULATE(
[Total Sales],
SAMEPERIODLASTYEAR('Calendar'[Date])
)
This measure returns the absolute increase or decrease in sales compared to the previous year.
🚀 Tip for Power BI Job Seekers:
CALCULATE() is the most important DAX function. Learn how it modifies the filter context because it's used in almost every advanced Power BI interview question.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
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🚀 Power BI Interview Challenge #4 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
Product
Sales
Create a DAX measure to calculate the percentage contribution of each product to total sales.
𝗠𝗲: Challenge accepted! 💪
Sales Contribution % =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
ALL(Sales[Product])
),
0
)
💡 Explanation:
This measure calculates how much each product contributes to the total sales.
• [Total Sales] returns the sales for the current product.
• ALL(Sales) removes the product filter while keeping other filters intact.
• CALCULATE() recalculates the total sales after removing the product filter.
• DIVIDE() safely performs the division and avoids divide-by-zero errors.
This challenge tests your understanding of:
✅ CALCULATE()
✅ ALL()
✅ DIVIDE()
✅ Filter Context
✅ Percentage Calculations
🎯 Expected Output Example
Product | Sales | Sales Contribution %
Laptop | 50,000 | 50%
Mouse | 20,000 | 20%
Keyboard | 15,000 | 15%
Monitor | 15,000 | 15%
🚀 Bonus (Dynamic Percentage by Selected Filters)
Sales Contribution % =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
ALLSELECTED(Sales[Product])
),
0
)
Using ALLSELECTED() respects slicers and page filters while removing only the product filter, making the measure more interactive.
🚀 Tip for Power BI Job Seekers:
Understanding the difference between these functions is crucial for interviews:
• ALL() → Removes all filters from the specified column or table.
• ALLSELECTED() → Respects user selections made through slicers and filters.
• REMOVEFILTERS() → Modern alternative to remove filters in many scenarios.
These are among the most frequently asked DAX concepts in Power BI interviews.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
Product
Sales
Create a DAX measure to calculate the percentage contribution of each product to total sales.
𝗠𝗲: Challenge accepted! 💪
Sales Contribution % =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
ALL(Sales[Product])
),
0
)
💡 Explanation:
This measure calculates how much each product contributes to the total sales.
• [Total Sales] returns the sales for the current product.
• ALL(Sales) removes the product filter while keeping other filters intact.
• CALCULATE() recalculates the total sales after removing the product filter.
• DIVIDE() safely performs the division and avoids divide-by-zero errors.
This challenge tests your understanding of:
✅ CALCULATE()
✅ ALL()
✅ DIVIDE()
✅ Filter Context
✅ Percentage Calculations
🎯 Expected Output Example
Product | Sales | Sales Contribution %
Laptop | 50,000 | 50%
Mouse | 20,000 | 20%
Keyboard | 15,000 | 15%
Monitor | 15,000 | 15%
🚀 Bonus (Dynamic Percentage by Selected Filters)
Sales Contribution % =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
ALLSELECTED(Sales[Product])
),
0
)
Using ALLSELECTED() respects slicers and page filters while removing only the product filter, making the measure more interactive.
🚀 Tip for Power BI Job Seekers:
Understanding the difference between these functions is crucial for interviews:
• ALL() → Removes all filters from the specified column or table.
• ALLSELECTED() → Respects user selections made through slicers and filters.
• REMOVEFILTERS() → Modern alternative to remove filters in many scenarios.
These are among the most frequently asked DAX concepts in Power BI interviews.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges!
❤10
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