Top 5 Data Analytics MCQs 🔥 | SQL Python AI Interview Questions #1

✅ Q1 → A. Structured Query Language 👉 SQL is used to interact with databases like MySQL, PostgreSQL, SQL Server. 🔹 Real Example: Imagine a table employees id name salary 1 A 50000 2 B 70000 🔹 Query: SELECT name, salary FROM employees WHERE salary v 60000; 👉 Output: B (70000) 🔹 Tools: MySQL PostgreSQL SQL Server BigQuery ✅ Q2 → B. Pandas 👉 Pandas is used for data manipulation and analysis. 🔹 Real Dataset Example: import pandas as pd data = { "name": ["A", "B", "C"], "salary": [50000, 70000, 60000] } df = pd.DataFrame(data) # Filter high_salary = df[df["salary"] v 60000] print(high_salary) 👉 Output: name salary 1 B 70000 🔹 Why not others? NumPy → numerical operations Matplotlib → visualization TensorFlow → machine learning ✅ Q3 → C. Data modeling and calculations (DAX) 👉 DAX is used inside Power BI for creating measures & calculated columns. 🔹 Real Example (Power BI DAX): Total_Sales = SUM(Sales[Amount]) 👉 Calculates total sales from dataset Average_Sales = AVERAGE(Sales[Amount]) 🔹 Use Case: Business dashboards KPI tracking Financial reporting 🔹 Tool: Power BI ✅ Q4 → D. Predictive Loading 👉 Not a real ML type. 🔹 Real ML Types: 1. Supervised Learning from sklearn.linear_model import LinearRegression model = LinearRegression() 👉 Used when labels are available (e.g., house price prediction) 2. Unsupervised Learning from sklearn.cluster import KMeans model = KMeans(n_clusters=3) 👉 Used for clustering (customer segmentation) 3. Reinforcement Learning 👉 Used in: Self-driving cars Game AI ✅ Q5 → B. Extract Transform Load (ETL) 👉 Core Data Engineering pipeline 🔹 Step 1: Extract import pandas as pd df = pd.read_csv("data.csv") 🔹 Step 2: Transform df = df.dropna() df["salary"] = df["salary"] * 1.1 🔹 Step 3: Load df.to_csv("cleaned_data.csv", index=False) 🔹 Real Tools: Apache Airflow Talend AWS Glue Azure Data Factory 🚀 Real-World Insight (Important 🔥) 👉 In companies: SQL → data ex