Pratique pour l'examen | Microsoft Azure DP-900

Prepare for Azure Data Fundamentals (DP-900) with Realistic Practice Questions!

This resource provides valuable practice questions to help you assess your readiness for the Microsoft Azure Data Fundamentals (DP-900) certification exam. It's designed to give you a realistic understanding of the types of questions you might encounter.

Important Disclaimer: These questions are not the official exam questions. They cover all the material outlined in the knowledge areas below. Many questions utilize realistic scenarios with embedded questions to simulate a real exam experience.

Content Updates & Accuracy: The official exam requirements are regularly updated. We strive to keep our content current, but updates are often made without prior notice. Expect potential changes to the questions and content.

English & French Terminology: To enhance your understanding of Azure terminology, many questions include both the English and French terms. This will help you recognize the components used within the Azure ecosystem.

Detailed Explanations & Resources: Each question comes with a detailed explanation and links to relevant reference documentation. This ensures you understand why an answer is correct, not just what the correct answer is.

Adaptive Testing: Questions are randomized each time you retake the test. This forces you to understand the underlying concepts, not just memorize answers.

Complementary Study Material: This course is not intended to be your sole study resource. It's designed to supplement your existing study materials.

Feedback Welcome: If you encounter any content that requires clarification, please send a screenshot with the question number. (Note: Question numbers change with each test execution and do not identify specific questions.)

Key Knowledge Areas Covered:

Core Data Concepts (25-30% of Exam):

• Data Representation: Understanding how data can be represented (structured, semi-structured, and unstructured).
• Structured Data: Characteristics and examples of structured data.
• Semi-structured Data: Characteristics and examples of semi-structured data.
• Unstructured Data: Characteristics and examples of unstructured data.
• Data Storage Options: Identifying various data storage options.
• File Formats: Common file formats for data storage.
• Database Types: Understanding different types of databases.
• Data Workloads: Distinguishing between common data workloads (transactional vs. analytical).
• Data Roles & Responsibilities: Understanding roles like database administrators, data engineers, and data analysts.

Relational Data on Azure (20-25% of Exam):

• Relational Concepts: Understanding fundamental relational database concepts.
• Relational Data Characteristics: Identifying the characteristics of relational data.
• Normalization: Understanding normalization and its purpose.
• SQL: Knowledge of common SQL language commands.
• Database Objects: Familiarity with common database objects.
• Azure Relational Database Services: Understanding Azure's relational database offerings.
• Azure SQL Family: Understanding Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs.
• Open Source Database Services: Identifying Azure database services for open-source databases.

Non-Relational Data on Azure (15-20% of Exam):

• Azure Storage: Understanding the capabilities of Azure Storage.
• Azure Blob Storage: Understanding Azure Blob Storage.
• Azure File Storage: Understanding Azure File Storage.
• Azure Table Storage: Understanding Azure Table Storage.
• Azure Cosmos DB: Understanding the capabilities and features of Azure Cosmos DB.
• Azure Cosmos DB Use Cases: Identifying appropriate use cases for Azure Cosmos DB.
• Azure Cosmos DB APIs: Understanding Azure Cosmos DB APIs.

Data Analytics on Azure (25-30% of Exam):

• Big Data Analytics: Understanding common elements of large-scale analytics.
• Data Ingestion & Processing: Understanding data ingestion and processing considerations.
• Analytical Data Stores: Understanding options for analytical data stores.
• Azure Data Warehousing Services: Understanding Azure services for data warehousing (Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, Azure HDInsight, Azure Data Factory).
• Real-time Data Analysis: Understanding real-time data analysis considerations.
• Batch vs. Streaming Data: Understanding the difference between batch and streaming data.
• Microsoft Cloud Analytics Services: Identifying Microsoft cloud services for real-time analytics.
• Power BI: Understanding data visualization in Microsoft Power BI.
• Power BI Features: Understanding Power BI features.
• Power BI Data Models: Understanding data modeling features in Power BI.
• Appropriate Visualizations: