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DAX QUERIES

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  šŸ§® DAX QUERIES – COMPLETE INTERVIEW GUIDE (AAS / Power BI) Applies to Azure Analysis Services and Power BI 1️⃣ DAX BASICS (They expect this instantly) ✅ Total Sales Total Sales = SUM ( FactSales[SalesAmount] ) šŸ—£ Say: “This is a simple aggregation evaluated in filter context.” ✅ Total Orders Total Orders = COUNT ( FactSales[OrderID] ) 2️⃣ CALCULATE – MOST IMPORTANT DAX FUNCTION šŸ”„ ✅ Sales for Current Year Sales CY = CALCULATE ( [Total Sales], DimDate[Year] = YEAR ( TODAY() ) ) šŸ—£ Senior explanation: “CALCULATE modifies the filter context before evaluating the measure.” ✅ Sales for a Specific Region Sales US = CALCULATE ( [Total Sales], DimRegion[Country] = "USA" ) 3️⃣ TIME INTELLIGENCE (GUARANTEED QUESTIONS) ✅ Year-to-Date (YTD) Sales YTD = TOTALYTD ( [Total Sales], DimDate[Date] ) ✅ Month-to-Date (MTD) Sales MTD = TOTALMTD ( [Total Sales], DimDate[Date] ) ✅ Previous Year Sales Sales PY = CALCULATE ( [T...
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Azure Analysis Services

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  šŸ”· Azure Analysis Services (AAS) – TIERS EXPLAINED 1️⃣ What Are AAS Tiers? Azure Analysis Services tiers define: Compute power Memory capacity Concurrent users Query performance Cost šŸ—£ Senior line: “AAS tiers help balance performance, concurrency, and cost based on BI workload.” 2️⃣ Azure Analysis Services Tier Types AAS has two main pricing tiers : Tier Purpose Developer (D) Development / Testing Basic (B) Small production workloads Standard (S) Enterprise production 3️⃣ Developer Tier (D1) Feature Details Use Case Dev / QA SLA ❌ No SLA Scale Fixed Cost Low šŸ—£ Interview explanation: “Developer tier is used for model development and testing, never production.” 4️⃣ Basic Tier (B1–B2) Feature B1 B2 Use Case Small prod Medium prod Memory Low Medium Concurrency Limited Moderate SLA ✅ Yes šŸ—£ When to use: “For limited users and simpler models.” 5️⃣ Standard Tier (S0–S9) ⭐ MOST IMPORTANT Feature Description Use Case Enterprise workloads Me...
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DATA MODELING

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  šŸ§± DATA MODELING – SENIOR DATA ENGINEER VIEW 1️⃣ Types of Data Models (They WILL ask) Model Purpose When to Use Conceptual Business view Early discussions Logical Entities & relationships Platform-agnostic Physical Tables, indexes Implementation šŸ—£ Say: “I usually start with logical modeling and then optimize physically based on query patterns.” 2️⃣ OLTP vs OLAP (Very Important) OLTP OLAP Normalized (3NF) Denormalized Frequent inserts/updates Read-heavy Small transactions Large scans App-driven Analytics-driven šŸ—£ Senior answer: “For analytics, I prefer dimensional models to reduce join complexity.” 3️⃣ Dimensional Modeling (⭐ MUST KNOW) šŸ”¹ Star Schema (Most Preferred) 4 Components Fact Table → Measures (sales, amount) Dimension Tables → Context (customer, product, date) FACT_SALES ----------- date_key customer_key product_key amount quantity šŸ—£ Say: “Star schema improves query performance and is BI-friendly.” šŸ”¹ Snowflake Schema šŸ—£ Say: “Snowflake reduces redundancy but in...
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