Microsoft Azure Data Fundamentals (DP-900) Exam Preparation

Data visualization in Microsoft Power BI is a crucial aspect of data analysis and reporting. Power BI offers a wide range of visualization tools and features that allow users to create interactive and insightful reports and dashboards. These visualizations include various chart types (e.g., bar charts, line charts, pie charts), maps, tables, and custom visuals. Power BI also provides features like drill-down capabilities, cross-filtering, and slicers, which enable users to explore data dynamically and gain deeper insights. Additionally, Power BI supports the creation of custom visuals and the use of R and Python scripts for advanced visualizations.

This topic is an essential component of the Microsoft Azure Data Fundamentals (DP-900) exam as it relates to the broader theme of data analytics and visualization in Azure. Understanding Power BI's visualization capabilities is crucial for candidates to demonstrate their knowledge of how to effectively present and analyze data using Microsoft's business intelligence tools. This topic aligns with the exam's focus on foundational concepts in data processing and visualization, which are key skills for professionals working with Azure data services.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of different visualization types available in Power BI and their appropriate use cases.
• Scenario-based questions asking candidates to select the most suitable visualization for a given data set or business requirement.
• Questions about Power BI features such as drill-down, cross-filtering, and slicers, and how they enhance data exploration and analysis.
• Multiple-choice or true/false questions on the capabilities and limitations of Power BI visualizations.
• Questions comparing Power BI visualizations with other Azure data visualization tools to assess candidates' understanding of the broader Azure ecosystem.

The depth of knowledge required will typically be at a foundational level, focusing on understanding key concepts and use cases rather than advanced implementation details.

A modern data warehouse is a crucial component of a comprehensive data analytics solution in Azure. It combines and integrates data from various sources, including relational databases, big data systems, and IoT devices. The key components of a modern data warehouse typically include Azure Synapse Analytics (formerly SQL Data Warehouse) for large-scale data processing and analytics, Azure Data Lake Storage Gen2 for scalable and secure data storage, Azure Data Factory for data integration and ETL processes, and Azure Databricks for big data analytics and machine learning. These components work together to provide a unified platform for storing, processing, and analyzing large volumes of structured and unstructured data, enabling organizations to derive valuable insights and make data-driven decisions.

This topic is essential to the Microsoft Azure Data Fundamentals (DP-900) exam as it covers one of the core concepts in modern data solutions. Understanding the components of a modern data warehouse is crucial for candidates to grasp how Azure services can be leveraged to build scalable and efficient data analytics solutions. This knowledge forms the foundation for more advanced topics in data processing, storage, and analysis within the Azure ecosystem.

Candidates can expect the following types of questions on this topic in the actual exam:

• Multiple-choice questions asking to identify the primary components of a modern data warehouse in Azure
• Scenario-based questions where candidates need to select the most appropriate Azure services for specific data warehousing requirements
• True/false questions about the capabilities and features of different components in a modern data warehouse
• Matching questions that require linking Azure services to their primary functions within a data warehouse architecture

The depth of knowledge required will typically focus on understanding the basic purpose and functionality of each component, rather than in-depth technical details or implementation specifics. Candidates should be prepared to demonstrate their understanding of how these components work together to form a comprehensive data warehousing solution in Azure.

Analytics workloads in Azure refer to the various data processing and analysis tasks performed on large datasets to extract insights and support decision-making. These workloads typically involve collecting, storing, processing, and analyzing data using different Azure services. Key components of analytics workloads include data ingestion, data storage, data processing, and data visualization. Azure provides several services to support these workloads, such as Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. These services enable organizations to perform batch processing, real-time analytics, and machine learning on their data at scale.

Understanding analytics workloads is crucial for the Microsoft Azure Data Fundamentals (DP-900) exam as it forms a significant part of the "Describe analytics core concepts" domain. This topic relates to the broader context of data analytics in Azure and helps candidates understand how different Azure services can be used to implement various analytics scenarios. It also ties into other exam topics such as describing relational and non-relational data, and understanding the fundamentals of data visualization.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions asking to identify the most appropriate Azure service for a given analytics scenario
• Scenario-based questions describing a business problem and requiring candidates to select the best analytics approach or service
• Questions testing knowledge of key features and capabilities of Azure analytics services
• Questions about the differences between batch processing and real-time analytics
• Basic questions on the components of an analytics workflow, such as data ingestion, processing, and visualization

The depth of knowledge required will be at a foundational level, focusing on understanding core concepts and use cases rather than detailed implementation or configuration.

Basic management tasks for non-relational data in Azure typically involve working with Azure Cosmos DB, Azure's multi-model NoSQL database service. These tasks include creating and managing databases, containers, and items within Cosmos DB. Key management activities involve setting up partitioning strategies, configuring consistency levels, and managing throughput (measured in Request Units). Additionally, understanding how to perform CRUD operations (Create, Read, Update, Delete) on documents or items within Cosmos DB is essential. Other important aspects include implementing security measures such as access control and encryption, as well as monitoring and optimizing database performance using Azure tools like Azure Monitor and Azure Advisor.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it covers fundamental concepts of working with non-relational data in Azure. Understanding these basic management tasks is essential for anyone working with Azure's data services, particularly in scenarios where flexible schema and high scalability are required. This knowledge forms a foundation for more advanced Azure data services and contributes to a well-rounded understanding of Azure's data ecosystem. The topic aligns with the exam's focus on core data concepts and how they are implemented in Azure services.

Candidates can expect several types of questions related to this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of Cosmos DB concepts, such as consistency levels or partition key selection.
• Scenario-based questions where candidates need to identify the appropriate management task for a given situation, such as choosing the right consistency level for a specific use case.
• True/False questions about capabilities and limitations of Cosmos DB and its management tasks.
• Questions comparing relational and non-relational data management tasks in Azure.
• Questions about basic Azure CLI or PowerShell commands for managing Cosmos DB resources.

The depth of knowledge required is foundational, focusing on understanding key concepts and their practical applications rather than deep technical implementation details.

Non-relational data workloads, also known as NoSQL workloads, are designed to handle large volumes of unstructured or semi-structured data that don't fit well into traditional relational database models. These workloads are characterized by their ability to scale horizontally, provide high performance, and handle diverse data types. In Azure, non-relational data workloads are typically managed using services like Azure Cosmos DB, Azure Table Storage, and Azure Blob Storage. These services offer flexibility in data models, support for various APIs (e.g., document, key-value, graph), and the ability to distribute data across multiple regions for improved availability and performance.

Understanding non-relational data workloads is crucial for the Microsoft Azure Data Fundamentals (DP-900) exam as it forms a significant part of the "Describe core data concepts" domain. This topic is essential for candidates to grasp the differences between relational and non-relational data storage, and to understand when to use each type of solution in various scenarios. It also ties into other exam objectives, such as describing analytics workloads and exploring data storage options in Azure, providing a foundation for understanding modern data architectures and cloud-based data solutions.

Candidates can expect several types of questions related to non-relational data workloads on the DP-900 exam:

• Multiple-choice questions testing knowledge of non-relational database concepts and Azure services
• Scenario-based questions asking candidates to identify the most appropriate non-relational data solution for a given use case
• True/false questions about the characteristics and benefits of non-relational data workloads
• Questions comparing and contrasting relational and non-relational data solutions
• Questions about specific Azure services like Cosmos DB, including their features and use cases

The depth of knowledge required will typically focus on fundamental concepts and basic understanding of Azure services, rather than in-depth technical details or implementation specifics.

Non-relational data offerings on Azure refer to various database services that do not follow the traditional relational database model. These include Azure Cosmos DB, Azure Table Storage, and Azure Blob Storage. Azure Cosmos DB is a globally distributed, multi-model database service that supports various data models such as document, key-value, graph, and column-family. Azure Table Storage is a NoSQL key-value store for rapid development using massive semi-structured datasets. Azure Blob Storage is designed for storing large amounts of unstructured object data, such as text or binary data.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it covers one of the core aspects of data storage and management in Azure. Understanding non-relational data offerings is essential for candidates to grasp the diverse data storage options available in Azure and how they differ from traditional relational databases. This knowledge is fundamental for making informed decisions about data storage solutions in various scenarios and is a key component of Azure's data platform capabilities.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of different non-relational data services in Azure and their characteristics
• Scenario-based questions asking candidates to choose the most appropriate non-relational data service for a given use case
• Questions comparing and contrasting non-relational data offerings with relational databases
• Questions about the specific features and capabilities of Azure Cosmos DB, Azure Table Storage, and Azure Blob Storage
• Basic questions on data models supported by non-relational databases (e.g., document, key-value, graph)

The depth of knowledge required will be at a foundational level, focusing on understanding the core concepts, use cases, and basic features of non-relational data offerings in Azure rather than in-depth implementation details.

Querying data using SQL (Structured Query Language) is a fundamental skill for working with relational databases. In the context of Azure Data Fundamentals, this topic focuses on understanding basic SQL query techniques to retrieve, filter, and manipulate data stored in Azure SQL Database or other SQL-based systems. Key concepts include SELECT statements for data retrieval, WHERE clauses for filtering, JOIN operations for combining data from multiple tables, and aggregate functions for summarizing data. Additionally, candidates should be familiar with sorting results using ORDER BY, grouping data with GROUP BY, and using basic subqueries to perform more complex operations.

This topic is crucial to the overall Microsoft Azure Data Fundamentals (DP-900) exam as it forms the basis for working with structured data in Azure. Understanding SQL query techniques is essential for data professionals working with Azure SQL Database, Azure Synapse Analytics, and other relational database services in Azure. It relates directly to the exam's focus on core data concepts and how to work with various data services in Azure. Proficiency in SQL querying is a foundational skill that supports other areas of the exam, such as data analytics and data visualization.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of SQL syntax and query structure
• Scenario-based questions where candidates need to select the appropriate SQL query to solve a given problem
• Questions asking to identify the correct order of clauses in a SQL statement
• Matching questions linking SQL functions or clauses with their purposes
• Questions about the expected results of given SQL queries

The depth of knowledge required will typically focus on fundamental concepts rather than advanced SQL techniques. Candidates should be comfortable reading and understanding basic to intermediate SQL queries and be able to identify the appropriate SQL constructs to use in various data retrieval scenarios.

Basic management tasks for relational data in Azure typically involve creating, updating, and deleting database objects, as well as managing data within tables. This includes tasks such as creating and modifying tables, views, and stored procedures, setting up primary and foreign key relationships, and implementing data integrity constraints. Additionally, it covers basic data manipulation operations like inserting, updating, and deleting records, as well as querying data using SQL statements. Understanding how to perform backups and restores, monitor database performance, and manage user access and permissions are also essential aspects of relational data management in Azure.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it forms the foundation for working with relational databases in Azure. It relates directly to the "Describe relational data workloads" section of the exam outline and ties into broader concepts of data storage and processing in Azure. Understanding these basic management tasks is essential for candidates to grasp how relational data is handled in Azure environments and how it differs from traditional on-premises database management.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of basic SQL commands and their functions
• Scenario-based questions asking candidates to identify the correct management task for a given situation
• True/false questions about the capabilities and limitations of Azure SQL Database management
• Questions comparing and contrasting management tasks in Azure with on-premises database management
• Questions about best practices for data integrity, backup, and security in Azure relational databases

The depth of knowledge required will be at a foundational level, focusing on understanding concepts and basic implementation rather than advanced troubleshooting or complex scenarios.

Relational Azure data services refer to the cloud-based database solutions provided by Microsoft Azure that follow the relational database model. The primary services in this category are Azure SQL Database, Azure SQL Managed Instance, and Azure SQL Server on Virtual Machines. These services offer scalable, secure, and fully managed database platforms that support SQL queries and transactions. Azure SQL Database is a fully managed Platform as a Service (PaaS) offering, while Azure SQL Managed Instance provides near 100% compatibility with on-premises SQL Server instances. Azure SQL Server on Virtual Machines allows for more control and customization as an Infrastructure as a Service (IaaS) solution.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it forms a core component of Azure's data services offerings. Understanding relational Azure data services is essential for candidates to grasp the fundamentals of data management in the Azure ecosystem. It relates directly to the exam's focus on cloud data concepts and Azure data services, providing a foundation for more advanced data-related certifications. Mastery of this topic demonstrates a candidate's ability to identify and understand the appropriate relational database solutions for various business scenarios in Azure.

Candidates can expect the following types of questions on this topic in the DP-900 exam:

• Multiple-choice questions testing knowledge of the features and capabilities of different relational Azure data services
• Scenario-based questions asking candidates to select the most appropriate relational data service for a given business requirement
• True/false questions about the characteristics and use cases of Azure SQL Database, Azure SQL Managed Instance, and Azure SQL Server on Virtual Machines
• Questions comparing and contrasting the different relational Azure data services
• Basic questions on SQL query concepts and their application in Azure relational databases

The depth of knowledge required will be at a foundational level, focusing on understanding the core concepts, use cases, and basic differences between the various relational Azure data services rather than in-depth technical implementation details.

Relational data workloads in Azure involve managing and processing structured data using relational database management systems (RDBMS). This topic covers the fundamental concepts of relational databases, including tables, relationships, and SQL queries. It also encompasses understanding various Azure services for relational data, such as Azure SQL Database, Azure SQL Managed Instance, and Azure Database for MySQL/PostgreSQL. Candidates should be familiar with basic database operations, data normalization, and the benefits of using relational databases for transactional and analytical workloads in cloud environments.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it forms the foundation for understanding data management in Azure. Relational databases are widely used in enterprise environments, and knowledge of these workloads is essential for anyone working with data in the cloud. The topic relates closely to other exam areas, such as data storage options in Azure and basic data analytics concepts, providing a comprehensive understanding of data management within the Azure ecosystem.

Candidates can expect various question types on this topic in the exam:

• Multiple-choice questions testing knowledge of relational database concepts and Azure services
• Scenario-based questions asking candidates to choose the most appropriate Azure service for a given relational data workload
• True/false questions on the characteristics and benefits of relational databases
• Questions comparing relational databases to other data storage options in Azure
• Basic SQL query interpretation questions to assess understanding of data manipulation in relational databases

The depth of knowledge required will focus on fundamental concepts and Azure-specific implementations rather than advanced database administration or complex query optimization.

Core data workloads in Azure refer to the fundamental data processing and management tasks that organizations typically perform. These workloads include transactional data processing, analytical data processing, and batch data processing. Transactional data processing involves handling real-time data operations, such as those in e-commerce systems or banking applications. Analytical data processing focuses on analyzing large volumes of data to derive insights and support decision-making. Batch data processing involves processing large amounts of data in scheduled intervals, often used for tasks like generating reports or updating databases.

Understanding the types of core data workloads is crucial for the Microsoft Azure Data Fundamentals (DP-900) exam as it forms the foundation for comprehending how Azure's data services can be applied to various business scenarios. This topic relates directly to the exam's objective of describing core data concepts, which is essential for grasping the broader Azure data ecosystem. It helps candidates understand how different Azure services cater to specific data processing needs and how they can be integrated to create comprehensive data solutions.

In the actual exam, candidates can expect questions on this topic in various formats:

• Multiple-choice questions asking to identify the most appropriate Azure service for a given data workload scenario
• Scenario-based questions describing a business problem and requiring candidates to determine the type of data workload involved
• True/false statements about characteristics of different data workload types
• Matching questions linking data workload types to their typical use cases or Azure services

The depth of knowledge required will typically involve understanding the basic characteristics of each workload type, their common use cases, and how they align with Azure's data services. Candidates should be prepared to demonstrate their ability to differentiate between transactional, analytical, and batch data processing scenarios in the context of Azure data solutions.

Data analytics core concepts encompass the fundamental principles and processes involved in extracting insights from data. This topic covers the different types of analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. It also includes understanding the data analytics lifecycle, which typically involves stages such as data collection, preparation, analysis, visualization, and interpretation. Key concepts within data analytics include statistical analysis, data mining, machine learning, and big data processing. Candidates should be familiar with common data analytics tools and technologies used in Azure, such as Azure Synapse Analytics, Azure Databricks, and Power BI.

This topic is crucial to the Microsoft Azure Data Fundamentals (DP-900) exam as it forms the foundation for understanding how data is processed and analyzed in Azure environments. It relates directly to the exam's focus on core data concepts and how they apply to cloud-based data solutions. A solid grasp of data analytics core concepts is essential for candidates to comprehend more advanced topics in the exam, such as implementing and provisioning data analytics solutions in Azure.

Candidates can expect the following types of questions on this topic:

• Multiple-choice questions testing knowledge of different types of analytics and their applications
• Scenario-based questions asking candidates to identify the most appropriate type of analytics for a given business problem
• Questions about the data analytics lifecycle and its various stages
• Multiple-choice or matching questions on key terms and concepts related to data analytics
• Case study questions requiring candidates to apply their understanding of data analytics concepts to real-world scenarios in Azure environments

The depth of knowledge required will typically be at a foundational level, focusing on understanding and recognizing concepts rather than in-depth implementation details. However, candidates should be prepared to apply these concepts to practical scenarios within Azure data services.