Microsoft Azure Data Fundamentals (DP-900)

B is correct.

Explanation: Azure Blob Storage stores massive amounts of unstructured data as binary large objects, or blobs, in the cloud. This matches the requirement for unstructured data stored as blobs.

A is incorrect: Azure Table Storage is a NoSQL solution that uses tables containing key/value data items. It does not store data as binary large objects.

C is incorrect: Azure Files creates cloud-based network shares for documents and other files. Use it when multiple users need access to a share, not when the requirement is blob storage.

D is incorrect: Microsoft OneLake is a data lake provisioned by Microsoft Fabric. The requirement is for nonrelational, unstructured data stored as blobs.

D is correct.

Explanation: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. This matches the requirement.

A is incorrect: Azure Files provides cloud-based network shares for multiple users. It is not described as a data lake solution.

B is incorrect: Azure Table Storage is a NoSQL solution that stores key/value data items in tables. It is not a data lake solution.

C is incorrect: Microsoft OneLake is provisioned by Microsoft Fabric and is built upon Azure Data Lake Gen 2. It is not the data lake solution built into Azure Storage itself.

A is correct.

Explanation: Microsoft Fabric automatically provisions OneLake, and OneLake is built upon Azure Data Lake Gen 2. This matches both parts of the requirement.

B is incorrect: OneLake is built upon Azure Data Lake Storage Gen2, but Azure Data Lake Storage Gen2 is not the item that Fabric automatically provisions.

C is incorrect: Azure Blob Storage stores unstructured data as blobs. It is not the data lake that Fabric automatically provisions.

D is incorrect: Azure Files provides cloud-based network shares. It is not the data lake that Fabric automatically provisions.

C is correct.

Explanation: Azure Files creates cloud-based network shares, like those found in on-premises organizations. These shares make documents and other files available to multiple users.

A is incorrect: Azure Blob Storage stores unstructured data as binary large objects. It is not described as a network share service.

B is incorrect: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. The requirement is for a network share, not a data lake.

D is incorrect: Azure Table Storage stores key/value data items in tables. It does not provide network shares for documents and files.

B is correct.

Explanation: Azure Table Storage is a NoSQL storage solution that uses tables containing key/value data items. This matches the requirement.

A is incorrect: Azure Files creates cloud-based network shares for multiple users. It does not store key/value data items in tables.

C is incorrect: Azure Blob Storage stores unstructured data as binary large objects. The requirement is for tables of key/value items.

D is incorrect: Azure Data Lake Storage Gen2 is a cloud-scale data lake solution built into Azure Storage. It is not described as a table-based key/value store.

B is correct.

Explanation: You can classify data as structured, semi-structured, or unstructured.

A is incorrect: Transactional and analytical describe types of data processing, not data formats.

C is incorrect: Relational and nonrelational describe types of databases, not data formats.

D is incorrect: Read-only and read-write describe how a system is accessed, not data formats.

A is correct.

Explanation: The specific file format used to store data depends on many factors.

B is incorrect: Data can be structured, semi-structured, or unstructured. The file format does not always have to be structured.

C is incorrect: The file format depends on many factors, not solely on the database type.

D is incorrect: Read-only describes a type of system access, not a requirement for all file formats.

A is correct.

Explanation: Relational databases are commonly used to store and query structured data.

B is incorrect: A read-only system for historical data or business metrics is associated with analytical data processing.

C is incorrect: The data in this scenario is structured, not unstructured.

D is incorrect: The data is structured, and a file format is not a data store used to query structured data in this context.

C is correct.

Explanation: The work performed by transactional systems is often referred to as Online Transactional Processing (OLTP).

A is incorrect: Analytical data processing typically uses read-only systems that store historical data or business metrics.

B is incorrect: Semi-structured data classification describes a data format, not the work of transactional systems.

D is incorrect: While relational databases can be used, the specific term for the work performed by transactional systems is OLTP.

B is correct.

Explanation: Analytical data processing typically uses read-only systems that store vast volumes of historical data or business metrics.

A is incorrect: OLTP is the work performed by transactional systems, not read-only systems for historical data.

C is incorrect: Structured data classification describes a data format, not a type of data processing.

D is incorrect: Relational databases are used to store and query structured data, but analytical data processing is the specific term for read-only systems storing vast volumes of historical data.

B is correct.

Explanation: A database administrator manages databases, assigns permissions to users, stores backup copies of data, and restores data after a failure. These are the tasks in the scenario.

A is incorrect: A data analyst explores and analyzes data to create visualizations and charts. This role does not manage permissions or backups.

C is incorrect: A data engineer manages data integration, data cleaning routines, and pipelines that move and transform data. Backup and restore for a database is not the primary task of this role.

D is incorrect: An AI engineer builds and integrates AI-powered features. This role does not own database permissions or recovery.

C is correct.

Explanation: An AI engineer builds and integrates AI-powered features into applications and data workflows. This role works with large language models, machine learning pipelines, and data sources.

A is incorrect: A database administrator manages database availability, security, backup, and recovery. This role does not build AI-powered features.

B is incorrect: A data analyst explores data and builds reports and visualizations. A data analyst can work with an AI engineer to surface AI-generated insights, but does not build the feature.

D is incorrect: A data engineer builds pipelines and data stores. A data engineer collaborates with the AI engineer to access and prepare the underlying data, but the AI engineer builds and integrates the feature.

A is correct.

Explanation: Azure SQL VM is a virtual machine with an installation of SQL Server. It allows maximum configurability and requires full management responsibility from the owner.

B is incorrect: Azure SQL Database is a fully managed platform-as-a-service (PaaS) database. Microsoft manages the underlying infrastructure, so it does not give maximum configurability.

C is incorrect: Azure SQL Managed Instance allows more flexible configuration than Azure SQL Database and has automated maintenance. It does not give the maximum configurability of a virtual machine.

D is incorrect: Azure Database for PostgreSQL is a managed service for the open-source PostgreSQL database system. It does not provide a SQL Server installation.

D is correct.

Explanation: Azure Data Explorer is a fully managed, standalone, big data analytics platform. It offers high-performance querying of log and telemetry data, including data with a timestamp attribute.

A is incorrect: Azure Stream Analytics is a real-time stream processing engine. It applies a query to an input stream and writes the results to an output. It is not described as a platform for querying log and telemetry data.

B is incorrect: Azure Storage tables provide key-value storage for applications that need to read and write data values quickly. They are not an analytics platform for log queries.

C is incorrect: Azure Cosmos DB is a global-scale nonrelational database system for JSON documents, key-value pairs, column-families, and graphs. It is not described as an analytics platform for timestamped log data.

B and C are correct.

Explanation: Azure Data Factory lets you define and schedule data pipelines that transfer and transform data. Data engineers use it to build ETL solutions that populate analytical data stores. Microsoft Fabric includes data ingestion and ETL with Fabric Data Factory.

A is incorrect: Azure Stream Analytics is a real-time stream processing engine. It captures a stream from an input, applies a query, and writes the results to an output. It is not described as a pipeline or ETL service for transactional data.

D is incorrect: Microsoft Purview provides enterprise-wide data governance and discoverability. You use it to map data and track data lineage. It does not build ETL pipelines.

C is correct.

Explanation: In a relational database, you model collections of entities from the real world as tables. Customers and products are two collections of entities, so each one maps to a table.

A is incorrect: A stored procedure encapsulates programmatic actions. It is not the structure that models a collection of entities.

B is incorrect: An index is a structure that exists in addition to tables. It does not replace tables as the way to model entities.

D is incorrect: SQL is used to communicate with a relational database. It is a language, not a structure that stores entities.

B and D are correct.

Explanation: Normalization is a schema design process that minimizes data duplication and enforces data integrity. Repeating the same address in many rows is the kind of duplication that this process reduces.

A is incorrect: Encapsulating programmatic actions is a purpose of other database structures, not a goal of normalization.

C is incorrect: Communicating with a relational database is the purpose of SQL. Normalization is a schema design process and does not provide a query language.

A is correct.

Explanation: SQL stands for Structured Query Language. It is used to communicate with a relational database, so both the application and the analysts can use it.

B is incorrect: Normalization is a schema design process that minimizes data duplication and enforces data integrity. It is not used to communicate with a database.

C is incorrect: A stored procedure is a database object that encapsulates programmatic actions. It is not the primary method used to communicate with the database.

D is incorrect: An index is a structure that helps improve the speed of access. It is not used to communicate with the database.

D is correct.

Explanation: A relational database can contain structures in addition to tables that optimize data organization, encapsulate programmatic actions, and improve the speed of access. These structures are views, stored procedures, and indexes.

A is incorrect: Entities, rows, and columns describe how data is modeled in tables. They are not additional database objects beyond tables.

B is incorrect: Normalization is a design process and SQL is a language. These are not database objects in the set of views, stored procedures, and indexes.

C is incorrect: Collections and documents are not the database objects that are described for a relational database in this scenario.

A is correct.

Explanation: Relational databases are a common way for transactional applications to store and manage data. A relational database fits the requirement to store and manage application data.

B is incorrect: Normalization is a schema design process. It is not a data store.

C is incorrect: A SQL statement is used to communicate with a relational database. It does not store data itself.

D is incorrect: A view is a structure within a relational database. It is not the primary data store for the application.

B is correct.

Explanation: With autoscale, you set a maximum RU/s. Azure Cosmos DB then adjusts capacity automatically within that range based on actual demand.

A is incorrect: Dedicated throughput is reserved exclusively for a single container. It does not describe automatic adjustment within a maximum RU/s range.

C is incorrect: Shared throughput is provisioned at the database level and shared across up to 25 containers. It does not describe automatic adjustment based on demand.

D is incorrect: Serverless has no capacity to provision upfront, and you pay per request. You do not set a maximum RU/s, and serverless accounts are limited to a single Azure region.

C is correct.

Explanation: A well-chosen partition key has many distinct values and spreads data evenly across those values. A customer ID with similar order counts per customer meets both conditions. This helps keep throughput balanced as the database grows.

A is incorrect: A single shared value puts all items in one logical partition. Each logical partition can hold up to 20 GB of data.

B is incorrect: Three values do not provide many distinct values. Data is distributed across only three logical partitions.

D is incorrect: Most items share one value, so the data is not spread evenly. This does not keep throughput balanced.

D is correct.

Explanation: If an application depends on complex multi-table joins, Azure SQL Database is better suited than Azure Cosmos DB.

A is incorrect: Azure Cosmos DB for NoSQL stores JSON documents. It is a good fit for flexible schema, global reach, and low latency, not for complex multi-table joins.

B is incorrect: Azure Cosmos DB for Table stores data as key-value pairs. It does not address the requirement for complex multi-table joins.

C is incorrect: Azure Synapse Analytics is an option for large-scale historical analytics. The scenario describes an application that depends on joins, not historical analytics.

B and D are correct.

Explanation: Azure Cosmos DB for MongoDB is compatible with MongoDB drivers and client libraries, so existing MongoDB applications can connect without significant code changes. Azure Cosmos DB for Table uses the same programming model as Azure Table Storage, so existing Table Storage applications can connect with minimal code changes.

A is incorrect: Azure Cosmos DB for Apache Gremlin is designed for graph data and uses the Gremlin query language. Neither application uses a graph model.

C is incorrect: Azure Cosmos DB for NoSQL is the native API. It stores JSON documents and uses a SQL-like syntax. It does not match the MongoDB or Table Storage programming models.

A is correct.

Explanation: Azure Cosmos DB for Apache Gremlin is designed for graph data. Entities are vertices and relationships are edges. Gremlin is the query language used to traverse and manipulate graph data. Fraud detection is a listed use case for graph databases.

B is incorrect: Azure Cosmos DB for Apache Cassandra uses a column-family storage model and CQL. It is a good fit for migrating an Apache Cassandra workload, not for modeling relationships as edges.

C is incorrect: Azure Cosmos DB for Table stores data as key-value pairs in tables. Each row is identified by a PartitionKey and RowKey combination, which does not represent vertices and edges.

D is incorrect: Azure Cosmos DB for MongoDB stores data in BSON format and uses MQL. It is a good choice for teams with MongoDB expertise or MongoDB workloads, not for graph data.

B is correct.

Explanation: The evidence supports B because data modeling and visualization are at the heart of business intelligence workloads supported by large-scale data analytics solutions. They support reporting and decision making in organizations.

A is incorrect: Data replication and backup scheduling are not identified as central to business intelligence workloads for reporting and decision making.

C is incorrect: Network routing and firewall configuration are infrastructure tasks, not the core activities for business intelligence reporting.

D is incorrect: Operating system patching and virtual machine sizing are infrastructure tasks, not data modeling and visualization activities.

A is correct.

Explanation: The evidence supports A because Microsoft Power BI is a suite of tools and services that forms a core workload of Microsoft Fabric. Power BI tools support data modeling, interactive report creation, publishing, and consumption.

B is incorrect: Semantic models structure data to support analysis, but they are not the suite of tools and services used to create, publish, and consume interactive reports.

C is incorrect: Fact tables are used within semantic models to organize measures, not the suite of tools used for reporting and publishing.

D is incorrect: Dimension tables are used within semantic models to organize dimensions, not the suite of tools used for reporting and publishing.

C is correct.

Explanation: The evidence supports C because analytical models are also called semantic models in Microsoft Fabric and Power BI. They structure data to support analysis.

A is incorrect: Pipeline model is not a term used in the evidence for an analytical model that structures data for analysis.

B is incorrect: Dashboard model is not a term used in the evidence for an analytical model.

D is incorrect: Storage model is not a term used in the evidence for an analytical model.

A is correct.

Explanation: The evidence supports A because Power BI includes an extensive set of built-in visualizations, which can be extended with custom and third-party visualizations.

B is incorrect: Converting the report to a static document does not extend the visualization options; Power BI supports interactive reports and extending visualizations.

C is incorrect: Replacing the semantic model with a dimension table relates to data modeling, not extending visualization options.

D is incorrect: Exporting data to a fact table relates to data modeling, not extending visualization options.

B and D are correct.

Explanation: The evidence supports B and D because semantic models organize measures and dimensions using fact tables, dimension tables, and relationships.

A is incorrect: Notebooks are not identified as elements used to organize measures and dimensions in a semantic model.

C is incorrect: Pipelines are not identified as elements used to organize measures and dimensions in a semantic model.

B is correct.

Explanation: Large-scale data analytics solutions combine conventional data warehousing used to support business intelligence with techniques used for big data analytics.

A is incorrect: Online transaction processing (OLTP) is used for transactional workloads, not for combining data warehousing and big data analytics.

C is incorrect: Relational data modeling is a technique used within data warehousing, but it does not describe the combination of warehousing and big data analytics.

D is incorrect: Data visualization is an element of analytics architecture used to present data, not the term for combining warehousing and big data analytics.

B and D are correct.

Explanation: Large-scale data analytics architecture generally includes data ingestion and processing, analytical data stores, analytical data models, and data visualization.

A is incorrect: A transactional database is typically part of an operational workload, not a general element of a large-scale analytics architecture.

C is incorrect: Application server backups are operational tasks and not a general element of a large-scale analytics architecture.

A is correct.

Explanation: Data is ingested into an analytical data store from one or more sources using pipeline-based, federated, or streaming approaches.

B is incorrect: Transactional replication is typically used for operational databases, not as a primary approach for ingesting data into an analytical data store.

C is incorrect: Data visualization is used to present data, not to ingest it into an analytical data store.

D is incorrect: Relational modeling is used to structure data, not to ingest it.

C is correct.

Explanation: Data warehouses and data lakes are the two common types of analytical data store. Hybrid lakehouse approaches draw on both types.

A is incorrect: A data visualization dashboard presents data; it is not a hybrid analytical data store.

B is incorrect: A data ingestion pipeline loads data into an analytical data store; it is not a hybrid store type.

D is incorrect: An analytical data model is a separate element of the architecture used to structure data, not a hybrid of a data warehouse and a data lake.

A and B are correct.

Explanation: A Microsoft Fabric data lakehouse can be used to ingest and analyze data.

C is incorrect: A data lakehouse is an analytical data store, not a hosting environment for web applications.

D is incorrect: A data lakehouse is designed for analytics workloads, not for processing online transactions.

A is correct.

Explanation: Streaming data can be processed in real time, or near real time, to reveal insights and trends or to enable immediate responses. This matches a perpetual stream of data that needs a quick reaction.

B is incorrect: Processing the data later as a group does not meet the requirement for immediate responses.

C is incorrect: A one-time export and analysis does not process a perpetual stream as the data arrives.

D is incorrect: A perpetual stream does not end, so waiting for it to end does not meet the requirement.

B and D are correct.

Explanation: There are two general ways to process data: batch processing and stream processing. Use this distinction when you decide how a solution handles incoming data.

A is incorrect: Schema normalization is a data modeling activity. It is not one of the two general ways to process data.

C is incorrect: Index tuning is a database optimization activity. It is not one of the two general ways to process data.

C is correct.

Explanation: A stream processing solution commonly includes an event source, stream processing, and a sink for results. The sink is the element that receives the output of the processing.

A is incorrect: An event source supplies events to the solution. It does not receive results.

B is incorrect: A batch schedule is not one of the common elements of a stream processing architecture.

D is incorrect: A data entry form is not one of the common elements of a stream processing architecture.

D is correct.

Explanation: Microsoft Fabric Real-Time Intelligence is a set of tools built into Microsoft Fabric for ingesting, processing, and analyzing streaming data. It also supports visualization and action.

A is incorrect: Spark Structured Streaming is a library built into Spark. It is not described as the set of tools built into Microsoft Fabric for this purpose.

B is incorrect: Delta Lake supports streaming and batch processing. It is not described as the set of Fabric tools for ingesting, processing, and analyzing streaming data.

C is incorrect: Batch processing is a general way to process data. It is not a set of tools built into Microsoft Fabric.

B is correct.

Explanation: Spark Structured Streaming is a library built into Spark that supports working with streaming data. It matches the requirement for a Spark library.

A is incorrect: Microsoft Fabric Real-Time Intelligence is a set of tools built into Microsoft Fabric. It is not a library built into Spark.

C is incorrect: A sink is an element of a stream processing architecture that receives results. It is not a library for working with streaming data.

D is incorrect: An event source is an element of a stream processing architecture that supplies events. It is not a library for working with streaming data.

A is correct.

Explanation: The evidence states that Azure supports multiple database services, enabling you to run popular relational database management systems, such as SQL Server, PostgreSQL, and MySQL, in the cloud.

B is incorrect: Azure supports PostgreSQL and MySQL as relational database services, not just through third-party virtual machines.

C is incorrect: Azure supports SQL Server natively in the cloud; it does not require on-premises hosting.

D is incorrect: Azure supports these systems as relational database management systems; they do not need to be converted to a non-relational format.

A is correct.

Explanation: The evidence states that Azure SQL is a collective term for a family of Microsoft SQL Server based database services in Azure.

B is incorrect: Azure data services for MySQL support the MySQL relational database system, not Microsoft SQL Server based services.

C is incorrect: Azure data services for PostgreSQL support the PostgreSQL relational database system, not Microsoft SQL Server based services.

D is incorrect: Azure SQL is the specific collective term for the family of Microsoft SQL Server based database services in Azure.

B and D are correct.

Explanation: The evidence states that Azure SQL is a collective term for a family of Microsoft SQL Server based database services in Azure.

A is incorrect: Azure SQL is a collective term for a family of services, not a single standalone database service.

C is incorrect: Azure SQL services are based on Microsoft SQL Server, not PostgreSQL.

A is correct.

Explanation: The evidence states that in addition to Azure SQL services, Azure data services are available for other popular relational database systems, including MySQL and PostgreSQL.

B is incorrect: Azure data services are available for MySQL and PostgreSQL, so the teams do not need to migrate to Microsoft SQL Server.

C is incorrect: MySQL and PostgreSQL are supported by separate Azure data services, not as part of the Azure SQL family.

D is incorrect: Azure data services are available for both MySQL and PostgreSQL.

A is correct.

Explanation: The evidence states that Azure supports a range of database services that you can use to support new cloud applications or migrate existing applications to the cloud.

B is incorrect: Azure database services can support both new cloud applications and migrated applications, not exclusively new ones.

C is incorrect: Azure database services can support both migrated applications and new cloud applications, not exclusively migrated ones.

D is incorrect: Azure supports a range of database services; there is no requirement to use a single database service for both new and migrated applications.