Adaface Sample Data Warehouse Questions

You are a data warehouse engineer at a marketing agency, managing a large-scale database that stores extensive data on customer interactions, campaign metrics, and market research. The database is used predominantly for complex analytical queries, such as segment analysis, trend identification, and campaign performance evaluation. These queries often involve aggregations, filtering, and joining over large datasets.
            
            The existing setup, using traditional row-oriented storage, is struggling with performance issues, particularly for ad-hoc analytical queries that span multiple tables and require aggregating large volumes of data.
            
            The main tables in the database are:
            
            - Customer_Interactions (millions of rows): Stores individual customer interaction data.
            - Campaign_Metrics (hundreds of thousands of rows): Contains detailed metrics for each marketing campaign.
            - Market_Research (tens of thousands of rows): Holds market research data and findings.
            
            Considering the nature of the queries and the structure of the data, which of the following changes would most effectively optimize the query performance for analytical purposes?

A: Normalize the database further by splitting large tables into smaller, more focused tables and creating indexes on frequently joined columns.
            B: Implement an in-memory database system to facilitate faster data retrieval and processing.
            C: Convert the database to use columnar storage, optimizing for the types of analytical queries performed in the marketing context.
            D: Create a series of materialized views to pre-aggregate data for common query patterns.
            E: Increase the hardware capacity of the server, focusing on faster CPUs and more RAM.
            F: Implement partitioning on the main tables based on commonly filtered attributes, such as campaign IDs or time periods.

As a senior data warehouse engineer at a large retail company, you are tasked with designing a multidimensional data model to support complex OLAP (Online Analytical Processing) operations for retail analytics. The company operates in multiple countries and deals with a wide range of products. The primary requirement is to enable efficient analysis of sales performance across various dimensions such as time, geography, product categories, and sales channels.
            
            The source data resides in a transactional system with the following tables:
            
            - Transactions (Transaction_ID, Date, Store_ID, Product_ID, Quantity, Unit_Price)
            - Stores (Store_ID, Store_Name, Country, Region)
            - Products (Product_ID, Product_Name, Category, Supplier_ID)
            - Suppliers (Supplier_ID, Supplier_Name, Country)
            
            You need to design a schema in the data warehouse that facilitates fast querying for aggregations and comparisons along the mentioned dimensions. Which of the following schemas would best serve this purpose?

A: A star schema with a central fact table linking to dimension tables for Time, Store, Product, and Supplier.
            B: A snowflake schema where dimension tables for Store, Product, and Supplier are normalized.
            C: A galaxy schema with separate fact tables for Transactions, Inventory, and Supplier Orders, linked to shared dimension tables.
            D: A flat schema combining all source tables into a single wide table to avoid joins during querying.
            E: An OLTP-like normalized schema to maintain data integrity and minimize redundancy.
            F: A hybrid schema using a star schema for frequently queried dimensions and a snowflake schema for less queried, more detailed dimensions.

As a senior data warehouse developer, you are tasked with optimizing query performance in a large-scale data warehouse that primarily stores transactional data for a global retail company. The data warehouse is facing significant performance issues, particularly with certain types of queries that are crucial for business operations. After analysis, you identify that the most problematic queries are those that involve filtering and aggregating transaction data based on time periods (e.g., monthly sales) and specific product categories.
            
            The main transaction table (Transactions) in the data warehouse has the following structure and characteristics:
            
            - Columns: Transaction_ID (bigint), Transaction_Date (date), Product_ID (int), Quantity (int), Price (decimal), Category_ID (int)
            - Row count: Approximately 2 billion rows
            - Most common query pattern: Aggregating Quantity and Price by Category_ID and Transaction_Date (e.g., total sales per category per month)
            - Current indexing: Primary key index on Transaction_ID, no other indexes
            
            Based on this information, which of the following approaches would most effectively optimize the query performance for the given use case?

A: Add a non-clustered index on Transaction_Date and Category_ID.
            B: Normalize the Transactions table by splitting Transaction_Date and Category_ID into separate dimension tables.
            C: Implement partitioning on the Transactions table by Transaction_Date, and add a bitmap index on Category_ID.
            D: Convert the Transactions table to use a columnar storage format.
            E: Create a materialized view that pre-aggregates data by Category_ID and Transaction_Date.
            F: Increase the hardware capacity of the data warehouse server, focusing on CPU and memory upgrades.