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DP-203 Develop data processing Practice Question

You are a data engineer at a manufacturing company. You need to process sensor data from IoT devices that arrive in real time. The data is sent to Azure Event Hubs. You need to aggregate the data over 5-minute windows and store the results in Azure Data Lake Storage Gen2 in Parquet format. The solution should minimize cost and use serverless components. Which solution should you use?

⚠ Common exam trap

DP-203 often tests the confusion between tumbling, hopping, and sliding windows, and whether the candidate recognizes that Stream Analytics is the serverless streaming option versus Databricks or Functions.

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

✓

Use Azure Stream Analytics to create a query with a tumbling window of 5 minutes, and output the results to Azure Data Lake Storage Gen2 in Parquet format.

Azure Stream Analytics is a fully managed, serverless real-time analytics service that natively supports tumbling windows and can output directly to Azure Data Lake Storage Gen2 in Parquet format. It minimizes operational cost and management overhead because there are no clusters to provision, and it integrates directly with Event Hubs as an input.

Answer analysis

Option-by-option breakdown

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

  • ✓

    Use Azure Stream Analytics to create a query with a tumbling window of 5 minutes, and output the results to Azure Data Lake Storage Gen2 in Parquet format.

    Why this is correct

    Stream Analytics provides a fully managed, serverless engine with native tumbling-window aggregation over Event Hubs input, and writes Parquet directly to Data Lake Storage Gen2. This satisfies the real-time 5-minute windowing, serverless and cost-minimisation constraints without provisioning clusters.

  • ✗

    Use Azure Databricks with Structured Streaming to read from Event Hubs, aggregate with a sliding window, and write to ADLS Gen2 in Parquet.

    Why it's wrong here

    Databricks clusters run continuously for Structured Streaming, incurring VM costs that violate the serverless and cost-minimisation requirements. Structured Streaming with sliding windows is the right choice when stateful, exactly-once processing across long windows is needed on dedicated compute.

  • ✗

    Use Azure Data Factory with a tumbling window trigger to run a pipeline every 5 minutes that copies data from Event Hubs to ADLS Gen2.

    Why it's wrong here

    A tumbling window trigger only schedules pipeline runs; it cannot aggregate streaming events within 5-minute windows, so events arriving between runs are missed or copied unaggregated. Data Factory suits scheduled batch movement between stores, not continuous stream processing.

  • ✗

    Use Azure Functions with an Event Hubs trigger to aggregate data in memory and write to ADLS Gen2.

    Why it's wrong here

    Azure Functions buffers state in memory per instance, so 5-minute tumbling windows across partitioned Event Hubs events are not aggregated correctly under scaling. Functions suit simple per-event processing; windowed streaming aggregation belongs to Stream Analytics or Spark Structured Streaming.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Microsoft exam blueprint

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