DP-900 Describe an analytics workload on Azure Practice Question
A marketing company collects real-time clickstream data from their website using Azure Event Hubs. They need to perform two tasks: (1) aggregate the number of clicks per advertising campaign every 5 minutes and display the results in a live dashboard, and (2) run complex historical queries on months of aggregated click data to identify trends. They want to minimize data movement and use serverless compute where possible. Which combination of Azure services should they use?
⚠ Common exam trap
Candidates often confuse batch processing tools like Azure Data Factory or HDInsight with real-time stream processing, and overlooking that Azure Synapse serverless SQL pool is the serverless option for historical queries, not Azure SQL Database.
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 Stream Analytics for live aggregation and Power BI for the dashboard; Azure Synapse Analytics (serverless SQL pool) for historical queries
Azure Stream Analytics is ideal for real-time aggregation of clickstream data from Event Hubs, outputting to Power BI for a live dashboard. Azure Synapse Analytics serverless SQL pool allows querying months of aggregated data stored in Azure Data Lake Storage without provisioning compute, minimizing data movement and using serverless compute.
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 for live aggregation and Power BI for the dashboard; Azure Synapse Analytics (serverless SQL pool) for historical queries
Why this is correct
This is correct because Azure Stream Analytics is a fully managed, serverless stream-processing engine that can run live aggregations—like 5-minute tumbling windows—over clickstream events and push results directly to Power BI for a real-time dashboard. For historical analysis, Azure Synapse Analytics serverless SQL pool can query Parquet files in the data lake without provisioning dedicated compute, enabling on-demand T-SQL queries over the same raw clickstream data. This combination cleanly separates the streaming path from the batch/historical path, which is exactly what the scenario requires.
- ✗
Azure Data Factory for live aggregation; Azure Analysis Services for historical queries
Why it's wrong here
Azure Data Factory is an orchestration and ETL service that moves and transforms data on a schedule or event trigger; it is not designed to perform low-latency, high-throughput stream aggregation like a dedicated stream processor. Azure Analysis Services is an OLAP engine that requires you to build and manage tabular models, and while it can query cached or imported data, it is not the right tool for directly querying raw Parquet files in a data lake. Therefore, this pairing misses both the real-time streaming requirement and the serverless historical-query requirement.
When this WOULD be correct
A company needs to orchestrate and move data from on-premises SQL Server to Azure Blob Storage on a nightly schedule, and then provide interactive analytics on that data using a tabular model. Azure Data Factory would handle the scheduled data movement, and Azure Analysis Services would host the semantic model for fast, interactive queries.
- ✗
Azure HDInsight (Spark) for both live and historical processing
Why it's wrong here
Azure HDInsight with Spark can technically process both live and historical data—using Structured Streaming for real-time and batch DataFrames for historical workloads—but it is not serverless. You must provision, configure, and manage a cluster, and you are billed for running VMs even when jobs are idle. For a marketing team that wants minimal operational overhead, serverless options are strongly preferred, making this answer incorrect even though the technology is capable.
When this WOULD be correct
An exam scenario where the company needs to perform complex, custom machine learning on streaming data (e.g., real-time anomaly detection) and batch processing on large historical datasets, and is willing to manage clusters for full control over the processing environment.
- ✗
Azure Functions for real-time aggregation; Azure SQL Database for historical queries
Why it's wrong here
Azure Functions can react to individual events, but it is not built for high-throughput, stateful stream processing with windowed aggregations; scaling thousands of function invocations per second to compute tumbling or hopping windows over clickstream data would be inefficient and complex to implement correctly. Azure SQL Database for historical queries would require you to provision and maintain a database, create schema, and ingest the Parquet files—this is not a serverless, direct-query approach. As a result, this option fails both the performance characteristics and the serverless simplicity the question expects.
When this WOULD be correct
A company needs to process individual click events with custom business logic (e.g., enrichment or transformation) in near real-time, and store results in a relational database for simple historical lookups. The question would emphasize low-latency, event-driven processing and a small data volume where serverless compute is not a priority.
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.
✓Azure Stream Analytics for live aggregation and Power BI for the dashboard; Azure Synapse Analytics (serverless SQL pool) for historical queriesCorrect answer▾
Why this is correct
This is correct because Azure Stream Analytics is a fully managed, serverless stream-processing engine that can run live aggregations—like 5-minute tumbling windows—over clickstream events and push results directly to Power BI for a real-time dashboard. For historical analysis, Azure Synapse Analytics serverless SQL pool can query Parquet files in the data lake without provisioning dedicated compute, enabling on-demand T-SQL queries over the same raw clickstream data. This combination cleanly separates the streaming path from the batch/historical path, which is exactly what the scenario requires.
✗Azure Data Factory for live aggregation; Azure Analysis Services for historical queriesWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is an orchestration and data movement service, not a real-time stream processing engine, so it cannot perform live aggregation of clickstream data. Azure Analysis Services is an OLAP engine for semantic models, not a serverless SQL query service for historical data, and it requires data to be moved into its own store.
★ When this WOULD be the correct answer
A company needs to orchestrate and move data from on-premises SQL Server to Azure Blob Storage on a nightly schedule, and then provide interactive analytics on that data using a tabular model. Azure Data Factory would handle the scheduled data movement, and Azure Analysis Services would host the semantic model for fast, interactive queries.
Why candidates choose this
Candidates may confuse Data Factory's data movement capabilities with real-time processing, and think Analysis Services is suitable for historical queries because it supports analytics, overlooking the need for serverless SQL querying and minimal data movement.
✗Azure HDInsight (Spark) for both live and historical processingWrong answer — click to see why▾
Why this is wrong here
HDInsight (Spark) is not serverless and requires cluster management, contradicting the requirement to minimize data movement and use serverless compute. Additionally, it is overkill for simple 5-minute aggregations and live dashboards compared to Stream Analytics.
★ When this WOULD be the correct answer
An exam scenario where the company needs to perform complex, custom machine learning on streaming data (e.g., real-time anomaly detection) and batch processing on large historical datasets, and is willing to manage clusters for full control over the processing environment.
Why candidates choose this
Candidates may think Spark is a one-size-fits-all solution for both streaming and batch, and overlook the serverless and minimal-management requirements of the question.
✗Azure Functions for real-time aggregation; Azure SQL Database for historical queriesWrong answer — click to see why▾
Why this is wrong here
Azure Functions is not designed for real-time stream aggregation at scale; it lacks native windowing and state management for 5-minute tumbling windows. Azure SQL Database is not serverless and requires manual scaling, increasing data movement for historical queries.
★ When this WOULD be the correct answer
A company needs to process individual click events with custom business logic (e.g., enrichment or transformation) in near real-time, and store results in a relational database for simple historical lookups. The question would emphasize low-latency, event-driven processing and a small data volume where serverless compute is not a priority.
Why candidates choose this
Candidates may associate Azure Functions with 'serverless compute' and assume it can handle real-time aggregation, overlooking its lack of built-in stream processing features. Azure SQL Database is a familiar choice for historical data, but they miss the 'minimize data movement' and 'serverless' requirements.
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?”
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 |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.