100% OFF- Azure DP-900: Microsoft Azure Data Fundamentals Mock Tests

Ace the DP-900 Exam: Dive Deep with Our Comprehensive Azure Data Fundamentals Practice Tests!

Eager to demonstrate your prowess in Azure data concepts and skills? The DP-900 exam is a pivotal stepping stone, encompassing a broad spectrum from data storage and processing to data analytics. But with the sheer breadth of the content, effective preparation is vital. That’s where we come in!

Our practice test series is more than just a collection of questions. It’s your ultimate toolset to conquer the DP-900. We’ve meticulously curated tests that not only mirror the actual exam’s format and difficulty but also provide real-world context. Every question presents a scenario, making you adept at tackling any challenge the real exam throws.

Why Our Series Stands Out:

• Genuine Exam Feel: Simulate the real DP-900 exam experience. No surprises on the D-day!
• Real-World Questions: Immerse in questions rooted in real-world scenarios, prepping you for anything.
• In-depth Explanations: Mistakes are the best teachers. Grasp detailed feedback for every question and solidify your knowledge.
• Varied Test Pool: Engage with multiple exams. More practice, more confidence!

Commit to your DP-900 success. Enlist the power of our practice tests and navigate your certification journey with unparalleled confidence!

Each exam in this practice test series has been crafted based on the exam content outline defined by Microsoft

• Describe core data concepts (25-30%) (12 – 15)
• Identify considerations for relational data on Azure (20-25%) (10 – 13)
• Describe considerations for working with non-relational data on Azure (15-20%) (7 – 10)
• Describe an analytics workload on Azure (25-30%) (12 – 15)

EXAM CONTENT OUTLINE:

1. Describe core data concepts (25–30%)

1.1. Describe ways to represent data

• Describe features of structured data
• Describe features of semi-structured
• Describe features of unstructured data

1.2. Identify options for data storage

• Describe common formats for data files
• Describe types of databases

1.3. Describe common data workloads

• Describe features of transactional workloads
• Describe features of analytical workloads

1.4. Identify roles and responsibilities for data workloads

• Describe responsibilities for database administrators
• Describe responsibilities for data engineers
• Describe responsibilities for data analysts

2. Identify considerations for relational data on Azure (20–25%)

2.1. Describe relational concepts

• Identify features of relational data
• Describe normalization and why it is used
• Identify common structured query language (SQL) statements
• Identify common database objects

2.2. Describe relational Azure data services

• Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
• Identify Azure database services for open-source database systems

3. Describe considerations for working with non-relational data on Azure (15–20%)

3.1. Describe capabilities of Azure storage

• Describe Azure Blob storage
• Describe Azure File storage
• Describe Azure Table storage

3.2. Describe capabilities and features of Azure Cosmos DB

• Identify use cases for Azure Cosmos DB
• Describe Azure Cosmos DB APIs

4. Describe an analytics workload on Azure (25–30%)

4.1. Describe common elements of large-scale analytics

• Describe considerations for data ingestion and processing
• Describe options for analytical data stores
• Describe Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Data Factory

4.2. Describe consideration for real-time data analytics

• Describe the difference between batch and streaming data
• Describe technologies for real-time analytics including Azure Stream Analytics, Azure Synapse Data Explorer, and Spark Structured Streaming

4.3. Describe data visualization in Microsoft Power BI

• Identify capabilities of Power BI
• Describe features of data models in Power BI
• Identify appropriate visualizations for data