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 Event Hubs, Azure Stream Analytics, Azure Synapse Analytics

  Why this is correct

  Event Hubs is a fully managed, partitioned streaming ingestion service that can absorb millions of events per second, while Stream Analytics executes continuous SQL-like queries over tumbling, hopping, and sliding windows to detect patterns and transform data. Synapse Analytics then serves as the columnar data warehouse, using dedicated or serverless SQL pools to run historical T-SQL analytics at scale. This forms an integrated hot path because every layer is purpose-built for real-time and analytic workloads with no need for custom cluster management.

• ✗

  Azure IoT Hub, Azure Data Factory, Azure SQL Data Warehouse

  Why it's wrong here

  IoT Hub is primarily for device management, not just ingestion. Data Factory is a batch orchestration tool, not designed for real-time processing. While SQL Data Warehouse (now Synapse SQL pool) is columnar, the processing is not real-time.

  When this WOULD be correct

  A company needs to ingest data from multiple on-premises databases, transform it using a visual interface, and load it into a cloud data warehouse for batch reporting. Azure Data Factory would orchestrate the ETL, and Azure SQL Data Warehouse would serve as the storage and query layer.

• ✗

  Azure Event Hubs, Azure Stream Analytics, Azure Cosmos DB

  Why it's wrong here

  Event Hubs and Stream Analytics correctly handle the streaming ingestion and real-time transformation, but Cosmos DB is a multi-model NoSQL database built for low-latency transactional apps, not columnar analytic workloads. Although Cosmos DB offers an analytical store, it maintains a row-oriented, document/key-value model with provisioned throughput, and data analysts would not get the cost-efficient, T-SQL-friendly data warehouse experience Synapse provides. Storing aggregated streaming results there is possible but weakens the columnar scan performance expected for extensive historical analysis.

• ✗

  Azure Blob Storage, Azure Databricks, Azure Table Storage

  Why it's wrong here

  Blob Storage is a durable object-store landing zone, but it has no native streaming ingest protocol, so it would need custom producers to write small blobs continuously, and Data Lake Storage's hierarchical namespace doesn't create an event stream. Databricks can process streaming data through Structured Streaming, but it requires managing a Spark cluster and writing code rather than using a declarative always-on SQL job. Table Storage is an Azure Storage key-value/entity store that lacks columnar compression, indexing, and SQL query engines, making it unsuitable for the dense analytical workloads the healthcare data analysts need.

  When this WOULD be correct

  A scenario where the company needs to perform advanced analytics (e.g., machine learning) on large volumes of unstructured data (e.g., log files) using Apache Spark, and storage requirements are flexible (not columnar SQL). The question would emphasize data science workloads over near real-time SQL reporting.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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