DP-900 Azure Data Fundamental Cert Prep In One Day 4 sets

Welcome to the best quality DP-900: Microsoft Azure Data Fundamentals Certification practice tests to help you prepare for your DP-900 exam without any extra effort.

Individuals curious about cloud computing and eager to understand how Azure handles data storage, processing, and analytics or those working with data in various capacities, such as data analysts, database administrators, and data engineers, who want to understand Azure data solutions or Professionals from non-technical backgrounds or other IT domains looking to transition into data-centric roles, where knowledge of Azure data fundamentals is valuable. Practice tests help assess your readiness for the actual DP-900 exam. By simulating this test exam , you can gauge your current level of knowledge and identify areas that require further study. Practice tests highlight areas of strength and weakness. By reviewing the questions you answered incorrectly, you can focus your study efforts on specific topics that need improvement. Engaging with practice tests provides a realistic exam experience, allowing you to become comfortable with the types of questions, wording, and difficulty level you'll encounter on the DP-900 exam.

Successfully completing practice tests can boost your confidence and motivation. As you see improvement in your scores and understanding of the material, you'll feel more prepared to tackle the actual exam.

Practice tests help you refine your time management skills by simulating the time constraints of the DP-900 exam. Learning to pace yourself during practice can enhance your efficiency on exam day.

Achieving high scores on practice tests confirms your understanding of Azure data fundamentals and validates your preparation efforts. It's a tangible measure of your progress towards DP-900 certification.

Skills measured

· Describe core data concepts (25–30%)

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

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

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

Functional groups

Describe core data concepts (25—30%)

Describe ways to represent data

· Describe features of structured data

· Describe features of semi-structured

· Describe features of unstructured data

Identify options for data storage

· Describe common formats for data files

· Describe types of databases

Describe common data workloads

· Describe features of transactional workloads

· Describe features of analytical workloads

Identify roles and responsibilities for data workloads

· Describe responsibilities for database administrators

· Describe responsibilities for data engineers

· Describe responsibilities for data analysts

Identify considerations for relational data on Azure (20—25%)

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

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

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

Describe capabilities of Azure storage

· Describe Azure Blob storage

· Describe Azure File storage

· Describe Azure Table storage

Describe capabilities and features of Azure Cosmos DB

· Identify use cases for Azure Cosmos DB

· Describe Azure Cosmos DB APIs

Describe an analytics workload on Azure (25—30%)

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

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

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