Microsoft Azure Data Fundamentals (DP-900)

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.