Microsoft Azure Data Fundamentals
Last Update May 2, 2024
Total Questions : 289
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| Exam Name | Microsoft Azure Data Fundamentals |
| Exam Code | DP-900 |
| Actual Exam Duration | 60 minutes |
| Expected no. of Questions in Actual Exam | 60 |
| Exam Registration Price | $99 |
| Official Information | https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900 |
| See Expected Questions | Microsoft DP-900 Expected Questions in Actual Exam |
| Take Self-Assessment | Use Microsoft DP-900 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
| Section | Weight | Objectives |
|---|---|---|
| Describe data ingestion and processing on Azure | 25-30% | - describe common practices for data loading - describe the components of Azure Data Factory (e.g., pipeline, activities, etc.) - describe data processing options (e.g., Azure HDInsight, Azure Databricks, Azure Synapse Analytics, Azure Data Factory) |
| Describe data visualization in Microsoft Power BI | 25-30% | - describe the role of paginated reporting - describe the role of interactive reports - describe the role of dashboards - describe the workflow in Power BI |
| Describe the components of a modern data warehouse | 25-30% | - describe Azure data services for modern data warehousing such as Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight - describe modern data warehousing architecture and workload |
| Describe analytics workloads | 25-30% | - describe transactional workloads - describe the difference between a transactional and an analytics workload - describe the difference between batch and real time - describe data warehousing workloads - determine when a data warehouse solution is needed |
| Identify basic management tasks for non-relational data | 25-30% | - describe provisioning and deployment of non-relational data services - describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI) - identify data security components (e.g., firewall, authentication, encryption) - identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls) - identify management tools for non-relational data |
| Describe non-relational data workloads | 25-30% | - describe the characteristics of non-relational data - describe the types of non-relational and NoSQL data - recommend the correct data store - determine when to use non-relational data |
| Describe non-relational data offerings on Azure | 25-30% | - identify Azure data services for non-relational workloads - describe Azure Cosmos DB APIs - describe Azure Table storage - describe Azure Blob storage - describe Azure File storage |
| Describe query techniques for data using SQL language | 25-30% | - compare Data Definition Language (DDL) versus Data Manipulation Language (DML) - query relational data in Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL |
| Identify basic management tasks for relational data | 25-30% | - describe provisioning and deployment of relational data services - describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI) - identify data security components (e.g., firewall, authentication) - identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls) - identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.) |
| Describe relational Azure data services | 15-20% | - describe and compare PaaS, IaaS, and SaaS solutions - describe Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines - describe Azure Synapse Analytics - describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL |
| Describe relational data workloads | 25-30% | - identify the right data offering for a relational workload - describe relational data structures (e.g., tables, index, views) |
| Describe types of core data workloads | 15-20% | - describe batch data - describe streaming data - describe the difference between batch and streaming data - describe the characteristics of relational data |
| Describe data analytics core concepts | 15-20% | - describe data visualization (e.g., visualization, reporting, business intelligence (BI)) - describe basic chart types such as bar charts and pie charts - describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive) - describe ELT and ETL processing - describe the concepts of data processing |
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TESTED 03 May 2024
