DP-900 Describe an analytics workload on Azure Practice Question
A retail company ingests clickstream data from its e-commerce website into Azure Event Hubs. They need to detect customer journey patterns in real time within seconds and also prepare aggregated data for daily trend reports stored in Azure Data Lake Storage Gen2. The real-time processing must handle high throughput and support complex temporal queries like sessionization. The daily aggregation should be cost-effective and use serverless compute. Which combination of Azure services should they use?
⚠ Common exam trap
Test-takers frequently confuse Azure Functions (serverless compute) with Azure Stream Analytics (dedicated stream processing) for real-time analytics, and assuming Azure Batch (parallel job execution) is equivalent to Azure Data Factory (orchestrated data integration) for batch aggregation, leading candidates to overlook the specific requirements for high-throughput temporal queries and serverless cost-effectiveness.
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 real-time processing and Azure Data Factory for daily batch aggregation
Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.
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 real-time processing and Azure Data Factory for daily batch aggregation
Why this is correct
Azure Stream Analytics is the correct real-time service here because it provides native complex event processing over streaming inputs like Event Hubs or IoT Hub, supporting temporal windows, sessionization, and reference data joins in a SQL-like language. Azure Data Factory complements it by orchestrating daily batch aggregation through serverless Data Flows or external compute, then loading results into Azure Data Lake Storage on a time-based schedule. This pairing cleanly separates low-latency streaming analytics from scheduled batch processing, which is exactly what this scenario requires.
- ✗
Azure Functions for real-time processing and Azure Databricks for daily batch aggregation
Why it's wrong here
Azure Functions is an event-driven compute service designed for individual, short-lived triggers (e.g., HTTP requests, queue messages) and lacks built-in streaming constructs like windowed aggregates, session windows, and watermarks. While it can scale, it requires custom code and external state management to handle high-throughput clickstream event processing, making it a poor fit for real-time complex event processing. Azure Databricks, on the other hand, requires an active cluster and is not serverless out of the box; using it for a simple daily batch aggregation adds unnecessary management overhead and does not natively provide Data Factory-style orchestration for recurring scheduled pipelines.
- ✗
Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation
Why it's wrong here
Azure Stream Analytics is indeed a strong choice for real-time processing, but Azure Batch is a managed service for running parallel, compute-intensive workloads such as HPC simulations and rendering jobs—not for serverless data transformation or scheduled orchestration. Azure Batch lacks built-in weekly/daily scheduling, pipeline dependencies, and data-movement connectors that a batch aggregation job would need. Thus, although the streaming half is correct, the batch half is unsuitable, so this combination does not satisfy the 'daily batch aggregation' requirement.
When this WOULD be correct
A question requiring large-scale parallel batch processing for complex transformations (e.g., video rendering, Monte Carlo simulations) where you need to manage a pool of VMs and control job scheduling, with no requirement for serverless compute.
- ✗
Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation
Why it's wrong here
Azure Data Lake Analytics is a batch processing service that executes U-SQL jobs over data already stored in Data Lake Storage; it has no capability for ingesting or querying live streaming events with low latency. Therefore, using it for 'real-time processing' is fundamentally incorrect because it cannot consume an event stream or provide continuous outputs. While Azure Data Factory would handle the daily batch aggregation correctly, the failed real-time component makes this option technically invalid for the stated clickstream ingestion scenario.
When this WOULD be correct
A question where the requirement is to run complex batch analytics (e.g., custom U-SQL scripts) on large datasets in Azure Data Lake Storage, and the batch orchestration is handled by Azure Data Factory, with no real-time streaming need.
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 real-time processing and Azure Data Factory for daily batch aggregationCorrect answer▾
Why this is correct
Azure Stream Analytics is the correct real-time service here because it provides native complex event processing over streaming inputs like Event Hubs or IoT Hub, supporting temporal windows, sessionization, and reference data joins in a SQL-like language. Azure Data Factory complements it by orchestrating daily batch aggregation through serverless Data Flows or external compute, then loading results into Azure Data Lake Storage on a time-based schedule. This pairing cleanly separates low-latency streaming analytics from scheduled batch processing, which is exactly what this scenario requires.
✗Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregationWrong answer — click to see why▾
Why this is wrong here
Azure Batch is not serverless and is designed for compute-intensive parallel batch jobs, not for cost-effective daily aggregation with serverless compute. Azure Data Factory with serverless SQL or Mapping Data Flows is the correct serverless batch option.
★ When this WOULD be the correct answer
A question requiring large-scale parallel batch processing for complex transformations (e.g., video rendering, Monte Carlo simulations) where you need to manage a pool of VMs and control job scheduling, with no requirement for serverless compute.
Why candidates choose this
Candidates may think 'Batch' implies batch processing for daily aggregation, but they overlook the serverless requirement and that Azure Batch is not serverless, unlike Data Factory's serverless capabilities.
✗Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregationWrong answer — click to see why▾
Why this is wrong here
Azure Data Lake Analytics is not designed for real-time processing; it is a batch analytics service that runs U-SQL jobs on data already in storage, making it unsuitable for handling streaming clickstream data within seconds.
★ When this WOULD be the correct answer
A question where the requirement is to run complex batch analytics (e.g., custom U-SQL scripts) on large datasets in Azure Data Lake Storage, and the batch orchestration is handled by Azure Data Factory, with no real-time streaming need.
Why candidates choose this
Candidates may think Azure Data Lake Analytics can process streaming data because 'Analytics' sounds real-time, or they may confuse it with Azure Stream Analytics due to similar names.
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 Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
About these practice questions
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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.