Question 858 of 820
DP-900 Describe core data concepts Practice Question
A data engineer needs to implement a solution that provides near real-time analytics on clickstream data. The data arrives as JSON events and must be queryable with sub-second latency using SQL-like queries. The solution should minimize operational overhead. Which Azure service should they use?
⚠ Common exam trap
Watch out — candidates often confuse Azure Stream Analytics (a real-time processing engine) with Azure Data Explorer (an interactive analytics database), failing to recognize that the requirement for 'sub-second latency using SQL-like queries' on stored data points to a query engine, not a stream processor.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Explorer
Azure Data Explorer (ADX) is designed for interactive analytics on large volumes of streaming and historical data with sub-second query latency using Kusto Query Language (KQL), which supports SQL-like syntax. It natively ingests JSON events, provides near real-time analytics, and minimizes operational overhead as a fully managed, serverless service.
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 Stream Analytics
Why it's wrong here
Azure Stream Analytics is a stream processing engine, not a queryable data store; it runs continuous queries that transform input streams and write results to sinks like Power BI, Event Hubs, or SQL Database. To analyze data directly, you would query those output locations, and the end-to-end latency from event ingestion to queryable result in a dashboard can exceed one second due to processing and sink indexing. It does not offer a direct sub-second query interface on the live data itself, so it fails the stated requirement.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services provides in-memory OLAP tabular models that are ideal for slicing, dicing, and aggregating business data, but it relies on batch or on-demand processing to load or refresh data. Streaming events must first be landed and processed into the model's partitions, which introduces multiple seconds or minutes of latency, making it unsuitable for real-time sub-second queries. Additionally, its DirectQuery mode still queries the underlying data source, not a streaming pipeline, and cannot deliver the performance of a purpose-built streaming analytics engine.
- ✗
Azure Synapse Analytics
Why it's wrong here
Azure Synapse Analytics is a cloud data warehouse and big data platform that uses dedicated or serverless SQL pools to run complex T-SQL queries over large volumes of stored data. Ingesting streaming data into a table—whether through Synapse Pipelines, Spark structured streaming, or a COPY statement—adds materialization latency, and even a clustered columnstore index cannot guarantee sub-second responses for high-velocity event queries. It is optimized for batch and interactive analytical workloads, not for serving near real-time queries directly on live data streams.
- ✓
Azure Data Explorer
Why this is correct
Azure Data Explorer (ADX) is a fully managed, high-performance analytics service built specifically for near real-time telemetry, logs, and time-series data, using the Kusto Query Language (KQL) to filter, aggregate, and join events. It ingests data directly from Event Hubs and IoT Hub with low latency, and its columnar index and sharding design support sub-second query responses on massive streams of append-only data. This combination of rapid ingestion, optimized storage, and fast query execution directly satisfies the requirement for a sub-second analytical solution on streaming data.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 24, 2026
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