UST Data Analyst Interview First-Round Questions

Cracking the UST Data Analyst Interview: First-Round Questions

If you’re gearing up for a data analyst interview at UST, you’ll need more than just technical know-how; clarity in explaining concepts is equally critical. Here’s how I tackled the first-round questions and prepared to ace the challenge!

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📋 Top Questions and My Approach

1️⃣ Self-Introduction

Your introduction sets the tone for the interview. Here’s my strategy:

• Start with a brief overview of your educational background.
• Highlight your relevant experience, focusing on roles where you leveraged data analysis, SQL, or Power BI.
• End with a mention of key projects, tools you’re proficient in, and what excites you about the role.

Example:
"Hi, I’m [Your Name], a data analyst with 3+ years of experience in leveraging SQL, Power BI, and Python to drive data-driven decisions. In my recent role, I designed dashboards to monitor KPIs, optimized queries for better performance, and collaborated with cross-functional teams to deliver actionable insights. I’m passionate about turning complex datasets into meaningful stories and am excited to bring this expertise to UST."

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2️⃣ Which Data Sources Have You Used in Your Projects?

I’ve worked with a variety of data sources, including:

• Relational Databases: SQL Server, MySQL, PostgreSQL
• Cloud Data Warehouses: Snowflake, Google BigQuery
• Files: Excel, CSV, JSON
• APIs: REST APIs to fetch real-time data
• Other Tools: SharePoint and flat files

Tip: Emphasize the versatility of your experience and provide examples.

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3️⃣ What is DAX? Explain in Detail.

DAX (Data Analysis Expressions) is a formula language used in Power BI, Excel, and Analysis Services to perform calculations on data models.

Key points to include:

• DAX is a functional language designed for creating measures, calculated columns, and custom tables.
• It’s optimized for relational data models and works with columnar databases.
• Common use cases: aggregations (SUM, AVG), time intelligence functions (YTD, MTD), and filtering.

Example Function:

DAX

SalesLastYear = CALCULATE(SUM(Sales[Amount]), SAMEPERIODLASTYEAR(Sales[Date]))  

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4️⃣ Difference Between a Dimension and a Measure in Power BI

• Dimension: Represents qualitative data (e.g., categories, products, regions). Used to slice and filter data.
• Measure: Represents quantitative, aggregated data (e.g., sum of sales, average profit). Calculated dynamically based on dimensions.

Analogy: Dimensions are like the “labels,” while measures are the “numbers” you analyze.

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5️⃣ Types of DAX Functions You’ve Used

Some common DAX functions I’ve utilized:

• Aggregate: SUM, AVERAGE, COUNT
• Filter: CALCULATE, FILTER
• Time Intelligence: YTD, QTD, SAMEPERIODLASTYEAR
• Logical: IF, SWITCH

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6️⃣ Difference Between MAX and MAXX Aggregate Functions

• MAX: Returns the maximum value in a column.
  Example: MAX(Sales[Amount])
• MAXX: Evaluates an expression for each row in a table and returns the maximum result.
  Example: MAXX(Sales, Sales[Quantity] * Sales[Price])

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7️⃣ How Does the SUM Function Differ From SUMX in Power BI?

• SUM: Performs a straightforward addition of all values in a column.
  Example: SUM(Sales[Amount])
• SUMX: Iterates through a table, evaluating an expression for each row and then sums up the results.
  Example: SUMX(Sales, Sales[Quantity] * Sales[Price])

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8️⃣ Finding Sales for Category A From a Table With Multiple Categories

DAX Solution:

DAX

SalesCategoryA = CALCULATE(SUM(Sales[Amount]), Sales[Category] = "A")  

Alternatively, use a Power BI filter or slicer to display only Category A.

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9️⃣ How Would a Matrix Visual Appear With Multiple Columns and Countries?

The matrix visual would display:

• Rows: Hierarchies such as Product -> Subcategory.
• Columns: Categories like Region -> Country.
• Values: Aggregated metrics such as sales or revenue.

The visual creates a drill-down experience, allowing users to explore data interactively.

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🔟 Write a SQL Query to Retrieve Specific Details

Example Question: Retrieve total sales and average sales per category for the last year.
Solution:

sql

SELECT  
    Category,  
    SUM(SalesAmount) AS TotalSales,  
    AVG(SalesAmount) AS AvgSales  
FROM Sales  
WHERE SaleDate >= DATEADD(YEAR, -1, GETDATE())  
GROUP BY Category;  

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✨ Pro Tips for Preparation

1. Practice Explaining: Be ready to explain your thought process for each question.
2. Hands-On Practice: Use tools like Power BI and SQL databases to practice real-world scenarios.
3. Mock Interviews: Simulate the interview environment to improve confidence.

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📣 Let’s Learn Together!

These questions provide a strong foundation for UST’s Data Analyst role. If you’ve faced similar challenges or have tips, share them in the comments below!

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