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Microsoft Certified: Azure Data Fundamentals (DP-900)

The Microsoft Certified: Azure Data Fundamentals (DP-900) validates a basic understanding of core data concepts and how they are implemented using Azure data services. It covers relational and non-relational data, as well as analytical workloads such as data warehousing and visualization. Achieving the symbol AZ_DP_900 demonstrates a professional's literacy in the fundamentals of cloud data management.




---------- Question 1
A company is developing a new application that will store customer reviews, product catalog information, and sensor data from IoT devices. Customer reviews often include free-form text and varying sentiment scores. Product catalog data has a fixed schema with fields like ProductID, Name, Price, and Description. Sensor data arrives as time-series events with different measurement types and varying attributes depending on the sensor. Which of the following best describes the combination of data types that the company will likely need to manage for this application?
  1. Entirely Structured Data
  2. Entirely Semi-structured Data
  3. A combination of Structured, Semi-structured, and Unstructured Data
  4. Unstructured Data only

---------- Question 2
A large retail chain wants to analyze its historical sales data, customer demographics, and social media sentiment to gain insights into purchasing behavior and marketing campaign effectiveness. This analysis involves processing petabytes of data from various sources, requiring complex transformations, aggregation, and machine learning model training. The resulting aggregated data needs to be stored in an optimized format for fast analytical queries by business intelligence tools. The company is seeking a unified analytics platform on Azure that can handle data ingestion, processing, and serve an analytical store, providing a comprehensive solution. Which Azure service is specifically designed as a unified analytics platform that encompasses these capabilities, from data integration to analytics and reporting?
  1. Azure Databricks
  2. Azure Synapse Analytics
  3. Microsoft Fabric
  4. Azure Data Lake Storage Gen2

---------- Question 3
A manufacturing plant uses sensors to monitor machinery performance. They collect vast amounts of telemetry data, including temperature, pressure, and vibration readings, every second. This data needs to be processed continuously to detect anomalies in real-time and trigger immediate alerts if a machine malfunctions, potentially preventing costly downtime. Simultaneously, historical data is processed nightly to generate aggregated reports on overall equipment effectiveness. Which two distinct types of data processing are described in this scenario?
  1. Batch processing for real-time alerts and streaming processing for historical reports.
  2. Streaming processing for real-time alerts and batch processing for historical reports.
  3. Transactional processing for both real-time alerts and historical reports.
  4. Analytical processing for real-time alerts and transactional processing for historical reports.

---------- Question 4
A media company needs to store a vast collection of video files, image assets, and large unstructured documents. These files are accessed frequently by content creators and also served to end-users globally via a web application. The company requires a highly scalable, durable, and cost-effective storage solution that can handle petabytes of data without requiring complex file system management. Which Azure storage service is most appropriate for this specific use case, and what is a key feature that makes it suitable?
  1. Azure File Storage, due to its server message block SMB protocol support for shared access.
  2. Azure Table Storage, because it is optimized for high-throughput access to structured data.
  3. Azure Blob Storage, specifically Block Blobs, due to its capability to store massive amounts of unstructured object data at scale.
  4. Azure Queue Storage, for its message queuing capabilities to facilitate asynchronous communication between components.

---------- Question 5
A large manufacturing corporation wants to modernize its data analytics capabilities. They have two primary needs: 1. Process historical production data, which arrives in large batches nightly, to generate comprehensive reports on operational efficiency and inventory levels for the previous day. This requires complex transformations and aggregations over massive datasets. 2. Monitor real-time sensor data from factory machinery to detect anomalies and predict equipment failures immediately, ensuring minimal downtime. This data streams continuously and requires rapid processing and alerting. Which set of Azure services is most appropriate for fulfilling both the nightly batch processing of historical data and the real-time anomaly detection from streaming data?
  1. Azure Stream Analytics for batch processing and Azure Synapse Analytics for real-time processing.
  2. Azure Data Factory for historical data ingestion and processing, and Azure Databricks for real-time stream processing.
  3. Azure Databricks for complex batch processing and Azure Stream Analytics for real-time stream processing.
  4. Microsoft Fabric for batch processing and Azure Data Lake Storage for real-time processing.

---------- Question 6
An enterprise is planning to migrate its on-premises SQL Server environment to Azure. They require a solution that provides the highest level of compatibility with their existing SQL Server features, including access to the underlying Operating System and the ability to install custom third-party software alongside the database engine. Which Azure SQL service should they select?
  1. Azure SQL Database which is a fully managed Platform-as-a-Service offering for modern cloud applications.
  2. Azure SQL Managed Instance which provides near-total compatibility with on-premises SQL Server but hides the OS.
  3. SQL Server on Azure Virtual Machines which is an Infrastructure-as-a-Service offering providing full administrative control.
  4. Azure Cosmos DB with the SQL API which allows for globally distributed multi-model data storage and high availability.

---------- Question 7
A global gaming company is developing a new online multiplayer game that needs a database capable of handling millions of concurrent users with very low latency reads and writes, regardless of their geographical location. The game data, such as player profiles, scores, and inventory, is schemaless and can evolve rapidly. The development team wants to leverage an API that provides rich query capabilities and supports document-oriented data models. Which Azure Cosmos DB API would be the most appropriate choice for this scenario?
  1. SQL (Core) API
  2. Cassandra API
  3. Gremlin API
  4. Table API

---------- Question 8
A corporation is designing a modern data warehouse architecture on Azure. They need a service that can ingest data from hundreds of different sources, perform complex data transformations using Apache Spark, and integrate seamlessly with a centralized data lake for large-scale analytics. Which Azure service is specifically designed as a unified analytics platform to meet these engineering requirements?
  1. Azure Databricks
  2. Azure SQL Database
  3. Azure Analysis Services
  4. Azure Data Box

---------- Question 9
You are designing a database for a university to store information about students, courses, and enrollments. Initially, you consider storing all student details, course details, and their enrollment information in a single, large table. However, you are concerned about data redundancy, potential update anomalies where changes to a course title might need to be applied in multiple places, and insertion anomalies if you cannot add a course without a student enrolled. To mitigate these issues and improve data integrity and efficiency, you decide to decompose this large table into smaller, related tables. Which of the following database design principles are you primarily applying in this scenario?
  1. Denormalization
  2. Data Virtualization
  3. Normalization
  4. Indexing

---------- Question 10
A company stores customer contact information including names, addresses, and phone numbers in a structured table format. They also collect social media posts and images from customer interactions, which are stored as files. Furthermore, they receive XML files from partners containing order details where the schema can vary slightly depending on the partner. Which of the following best categorizes these three types of data respectively?
  1. Structured, Unstructured, Semi-structured
  2. Structured, Semi-structured, Unstructured
  3. Unstructured, Structured, Semi-structured
  4. Semi-structured, Unstructured, Structured

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