DP-900 - Describe an Analytics Workload on Azure (29% of the exam) - Section 4.1

Describe common elements of large-scale analytics including Azure Synapse Analytics, Microsoft Fabric, and data lakes.

Describe how Azure Synapse Analytics unifies a data lake, dedicated SQL pools, and Apache Spark pools, and how Microsoft Fabric brings these together as a single software-as-a-service analytics platform built on a shared data lake. Identify the role each component plays across ingestion, transformation, and query.

Azure Synapse AnalyticsMicrosoft Fabricdata lakededicated SQL poolsApache Spark

Practice question for this objective

Free sampleDescribe an Analytics Workload on Azuremedium

A data science team needs a collaborative workspace built on Apache Spark for large-scale data processing and machine learning. Which Azure service is designed for this?

  • AMicrosoft Power BI, which is the platform for building interactive reports and dashboards from data
  • BAzure Blob storage, which stores large volumes of unstructured files but does not process them
  • CAzure Databricks, an Apache Spark based platform for big-data processing and machine learning Correct
  • DMicrosoft Fabric Warehouse, which serves a relational SQL layer rather than Spark based processing
Azure Databricks is the Apache Spark based platform for large-scale data processing and machine learning on Azure. Azure Databricks is an Apache Spark based analytics platform that gives data engineers and data scientists a collaborative workspace for large-scale data processing and machine learning.

Why A is wrong: Power BI is for visualising and reporting on data, not running large-scale Spark data processing and machine learning.

Why B is wrong: Blob storage holds data but provides no Spark compute for processing or machine learning.

Why C is correct: Correct. Azure Databricks is an Apache Spark based analytics platform that gives data engineers and data scientists a collaborative workspace for large-scale data processing and machine learning.

Why D is wrong: A Fabric warehouse provides a relational SQL query layer, not a collaborative Spark workspace for data science.

See more DP-900 practice questions, answers explained.

Exam traps in Describe an Analytics Workload on Azure

Answers that look right on this material and are not. Each one is a distractor from a different question in the DP-900 bank for this domain.

  • A relational engine for transactional workloads where data is normalised for fast row writes

    Why it is wrong: That describes a transactional database tuned for writes, not Fabric, which is built for analytics across shared OneLake storage.

  • Apache Kafka, used as a streaming event source for real-time ingestion pipelines

    Why it is wrong: Apache Kafka is named only as an event source that streaming ingestion connects to, not the engine Databricks is built on.

  • A relational dedicated SQL pool that reserves fixed compute to store warehouse tables for a workspace

    Why it is wrong: A dedicated SQL pool is a Synapse compute model, not OneLake; OneLake is storage, not a reserved-compute SQL warehouse.

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