20+ practice questions focused on Describe an analytics workload on Azure — one of the most tested topics on the Microsoft Azure Data Fundamentals DP-900 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Describe an analytics workload on Azure PracticeA logistics company receives real-time GPS tracking data from its delivery fleet via Azure Event Hubs. The data is a continuous stream of location updates (vehicle ID, latitude, longitude, timestamp). Additionally, the company has daily static route plan files in CSV format stored in Azure Data Lake Storage Gen2. The operations team needs to combine the live GPS stream with the route plans to create a near real-time dashboard showing if delivery vehicles are on schedule. They also want to run historical queries on both the stream data and route plans using T-SQL, without moving the data to another store. Which Azure service should they use as the primary analytics platform?
Explanation: Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can combine real-time streaming data from Azure Event Hubs (by first landing it in ADLS via Event Hubs Capture or a Synapse Pipeline) with static CSV route plans in Azure Data Lake Storage Gen2. It supports T-SQL queries directly against the files in ADLS using SQL Serverless, enabling near real-time dashboards and historical analysis without moving data to a separate store.
A retail company has an Azure SQL Database that handles OLTP transactions for its e-commerce platform. The analytics team needs to run complex reporting queries that join multiple tables (e.g., orders, products, customers) and aggregate millions of rows. These queries are long-running and would negatively impact the performance of the OLTP database if run directly. The company wants to use a separate analytics service that supports T-SQL queries, can scale compute independently, and provides a serverless option to avoid provisioning fixed resources. Which Azure service should they choose?
Explanation: Azure Synapse Analytics offers both dedicated and serverless SQL pools. The serverless SQL pool supports T-SQL queries, scales compute independently, and does not require provisioning fixed resources, aligning perfectly with the requirement. While the dedicated pool is provisioned, the Synapse service as a whole includes the serverless option, making it the correct choice.
A retail company needs to build an analytics pipeline on Azure. They ingest sales data from multiple store systems and an online e-commerce platform. The data must be cleaned, transformed, and loaded into a data warehouse for reporting. The company wants to use a modern ELT (Extract, Load, Transform) approach where raw data is stored first and then transformed. Order the following steps in the correct sequence for this pipeline. (Drag the steps into the correct order.)
Explanation: In an ELT (Extract, Load, Transform) approach, data is first extracted from source systems (A) and loaded as raw data into a scalable storage like a data lake (B). Once loaded, the raw data is transformed and cleaned (C). Finally, the transformed data is loaded into a data warehouse (D) for reporting and analysis (E).
A company is building a modern data warehouse on Azure using a lakehouse approach. Arrange the following steps in the correct order to implement a typical pipeline that starts with raw data ingestion and ends with business reporting.
Explanation: The explanation correctly describes the typical lakehouse pipeline order: Ingestion -> Transformation -> Loading -> Reporting. The steps A, B, C, D, as listed, are indeed in this correct logical sequence. However, the question format does not allow for selecting this sequence as an answer.
A data engineering team wants to build a batch analytics pipeline. The raw data is stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The final output will be a set of tables in Azure Synapse Analytics (dedicated SQL pool) that will be used to create reports in Power BI. Arrange the following steps in the correct order for a typical ETL process.
Explanation: The explanation states 'extract raw data to ADLS', which contradicts the stem's premise that raw data is already stored in Azure Data Lake Storage Gen2. The first step (A) correctly describes ingesting/extracting data *from* ADLS Gen2. Furthermore, the explanation introduces an additional transformation step 'within Synapse' after loading into staging ('load into Synapse staging, then transform within Synapse to final tables'). This implies an ELT pattern or multi-stage transformation not explicitly detailed in the provided options A, B, C, which describe a simpler ETL flow where transformation is completed in Databricks (B) before loading the final transformed data into Synapse (C). A more accurate explanation aligning with the provided steps would be: 'A typical data pipeline for this scenario follows the order: Ingest raw data from ADLS Gen2 into a processing engine like Azure Databricks (A), transform the data (B), load the transformed data into Azure Synapse Analytics (C), and finally create Power BI reports (D).'
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