DP-900 Practice Test

Answer choices

1. A. A single table named 'Orders' containing columns for CustomerID, OrderDate, ProductID, and Quantity.
2. B. Two tables: 'Customers' (CustomerID, Name, Address) and 'Orders' (OrderID, CustomerID, OrderDate).
3. C. Three tables: 'Customers' (CustomerID, Name, Address), 'Orders' (OrderID, CustomerID, OrderDate), and 'OrderProducts' (OrderID, ProductID, Quantity).
4. D. Four tables: 'Customers' (CustomerID, Name, Address), 'Products' (ProductID, ProductName, Price), 'Orders' (OrderID, CustomerID, OrderDate), and 'OrderDetails' (OrderID, ProductID, Quantity).

Wrong-answer review

• A. A single table named 'Orders' containing columns for CustomerID, OrderDate, ProductID, and Quantity.: A single Orders table repeats customer and product references across order lines.
• B. Two tables: 'Customers' (CustomerID, Name, Address) and 'Orders' (OrderID, CustomerID, OrderDate).: Customers and Orders alone do not store which products and quantities are included in each order.
• D. Four tables: 'Customers' (CustomerID, Name, Address), 'Products' (ProductID, ProductName, Price), 'Orders' (OrderID, CustomerID, OrderDate), and 'OrderDetails' (OrderID, ProductID, Quantity).: Adding a Products table duplicates the existing product-detail table described in the scenario.

Answer choices

1. A. Implement a clustered columnstore index on the `Sales` table.
2. B. Enable PolyBase to query data directly from Azure Data Lake Storage Gen2.
3. C. Utilize partition pruning by ensuring query predicates include the `TransactionDate` column.
4. D. Increase the dedicated SQL pool's Data Warehouse Units (DWUs).

Answer choices

1. A. Create multiple Power BI datasets, each connected to a different Azure Data Lake Storage Gen2 account and manually refresh them.
2. B. Utilize Azure Data Factory to copy data from all storage accounts into a single Azure Data Lake Storage Gen2 account and then connect Power BI to that central location.
3. C. Create OneLake shortcuts pointing to the data in the existing Azure Data Lake Storage Gen2 accounts, allowing Power BI and other Fabric components to access the data through a unified namespace.
4. D. Implement a custom solution using Azure Functions to dynamically generate a list of available data files and provide it to Power BI users.

Answer choices

1. A. Comma-Separated Values (CSV)
2. B. JavaScript Object Notation (JSON)
3. C. Extensible Markup Language (XML)
4. D. Tab-Separated Values (TSV)

Answer choices

1. A. Azure SQL Database General Purpose
2. B. Azure SQL Database Business Critical
3. C. Azure SQL Database Hyperscale
4. D. Azure SQL Database Basic

Answer choices

1. A. Use a partition key that places all products in one logical partition for simpler queries.
2. B. Manually assign a fixed number of physical partitions before loading the data.
3. C. Choose a partition key that distributes data and request volume evenly across logical partitions.
4. D. Bypass partition keys by spreading data across multiple unrelated Cosmos DB accounts.

Answer choices

1. A. Utilize a single Azure SQL Database table to store all data, including employee details, payment history, deductions, and scanned documents as BLOB data.
2. B. Implement a hybrid approach: Use Azure SQL Database for structured data (employee details, payment history, deductions) and Azure Data Lake Storage Gen2 for unstructured data (scanned documents).
3. C. Employ Azure Cosmos DB with a wide-column store to handle both structured and unstructured data, leveraging its schema flexibility.
4. D. Store all data, both structured and unstructured, in Azure Blob Storage, using a flat file structure for organization.

Answer choices

1. A. Converting the data to JSON format.
2. B. Compressing the data using gzip.
3. C. Converting the data to Parquet format.
4. D. Partitioning the data by date.

Answer choices

1. A. Create a series of individual data flows in Power BI Desktop, publishing each flow to the Fabric workspace and scheduling them to run sequentially.
2. B. Utilize a Fabric Data Factory pipeline, defining activities to copy data from various sources, transform it using data flows, and load it into the data lake.
3. C. Develop custom Python scripts using Azure Functions triggered by a timer, performing data extraction, transformation, and loading directly into the data lake.
4. D. Implement a combination of Azure Logic Apps and Azure Functions to handle data ingestion and transformation, orchestrating the workflow through Logic Apps.

Answer choices

1. A. Azure SQL Database
2. B. Azure Blob Storage
3. C. Azure Cosmos DB (DocumentDB API)
4. D. Azure Table Storage