DP-900 Describe core data concepts Practice Question
A manufacturing company collects sensor data from thousands of IoT devices. Each reading contains a device ID, timestamp, value, and device-specific measurement fields. The company needs to analyze the data in real time to detect anomalies and trigger alerts. They also need to store the same data for historical batch analysis to identify long-term trends. Which architecture pattern best describes this combination of data processing approaches?
⚠ Common exam trap
Candidates often confuse a storage architecture (data lake) with a processing architecture pattern, or mistakenly think that either stream or batch processing alone can satisfy both real-time and historical requirements.
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
✓
Lambda architecture
The Lambda architecture is the correct pattern because it combines both stream processing for real-time anomaly detection and alerting, and batch processing for historical analysis of long-term trends. This architecture uses a speed layer for low-latency stream processing (e.g., Apache Kafka, Azure Stream Analytics) and a batch layer for comprehensive, accurate historical computations (e.g., Azure Data Lake, Apache Spark). The serving layer then merges results from both paths to provide a unified view.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch processing only
Why it's wrong here
Batch processing only would compute results on a recurring schedule, such as hourly or daily, so anomaly detection on live sensor data would be delayed until the next run. In a manufacturing plant, this latency could allow defective parts or equipment failures to go unnoticed between batches. While batch processing is efficient for cleaning and aggregating historical data to identify long-term trends, it cannot meet the real-time alerting requirement without an additional stream-processing path.
- ✗
Stream processing only
Why it's wrong here
Stream processing only would handle individual sensor events with low latency and can trigger immediate anomaly alerts, but it is not designed to efficiently recalculate aggregates over months of stored telemetry or support ad hoc historical queries. Stream engines typically keep state in bounded time windows, so long-range trend analysis across thousands of devices would require exporting data to a separate store and rebuilding the analysis. Because the requirement explicitly includes both real-time detection and historical batch analysis, a stream-only architecture leaves the batch workload unsupported.
- ✓
Lambda architecture
Why this is correct
Lambda architecture is correct because it deliberately combines a batch layer for accurate, comprehensive historical processing and a speed layer for real-time stream processing over the same sensor data. The batch layer computes precise trends and baseline models from all collected data, while the speed layer provides low-latency anomaly detection and feeds both results into a serving layer for unified querying. This design satisfies both the real-time alerting and historical analysis requirements, with the tradeoff of maintaining two separate code paths.
- ✗
Data lake
Why it's wrong here
A data lake is a storage repository that can hold raw data in its native format, but it is not an architecture for processing data; it does not inherently provide stream or batch processing capabilities.
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
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Batch processing
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
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