DP-900 Describe an analytics workload on Azure Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Azure Databricks

  Why this is correct

  Azure Databricks is a unified analytics platform built on Apache Spark. While it supports both batch and real-time streaming, its core capability for distributed in-memory data processing makes it a primary choice for big data batch workloads such as large-scale ETL, data transformation, and machine learning over historical data. It manages clusters automatically and provides Databricks File System (DBFS) and Delta Lake for reliable, high-throughput batch jobs.

• ✗

  Azure Data Explorer

  Why it's wrong here

  Azure Data Explorer is a fast and highly scalable data analytics service designed specifically for interactive exploration of telemetry, logs, and time-series data. It uses Kusto Query Language (KQL) and a columnar storage engine to answer ad-hoc queries on high-velocity streaming or append-only data in near real-time, but it is not intended for complex multi-stage batch processing pipelines like distributed ETL, where the data shape and query patterns are unpredictable. Its strengths lie in live dashboards and time-based analysis rather than offline batch transforms.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is a real-time stream processing engine that executes continuous, declarative SQL-like queries over data flowing from sources such as Event Hubs, IoT Hub, or Blob storage. It provides low-latency event processing with temporal windowing (tumbling, hopping, sliding) and is inherently designed for never-ending streaming inputs rather than finite, on-demand batch datasets. It cannot natively handle batch orchestration or fault-tolerant multi-step job chains; that role is better filled by services like Databricks or HDInsight.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is an enterprise OLAP engine that hosts in-memory tabular and multidimensional semantic models for business intelligence. It performs pre-aggregation and fast querying against data that has already been transformed and loaded, but it does not process raw big data itself. It is a consumption layer that serves interactive BI reports and cubes, not a compute engine for distributed batch processing of massive datasets, so it falls outside the scope of big data batch pipelines.

• ✓

  Azure HDInsight

  Why this is correct

  Azure HDInsight is a fully managed cloud service that runs open-source big data frameworks such as Apache Hadoop, Spark, Hive, and HBase on autoscale-enabled clusters. It is explicitly tailored for batch processing: you can submit MapReduce jobs, Spark jobs, or Hive queries that process large volumes of data stored in Azure Data Lake Storage or Blob Storage in a distributed manner. It provides the same batch-oriented paradigm as on-premises Hadoop, making it a direct answer for this question alongside Databricks.