The answer is Lambda architecture. This is the correct choice because it is the only pattern that explicitly combines both stream processing for real-time anomaly detection and alerting, and batch processing for historical analysis of long-term trends, using a speed layer for low-latency data (e.g., Azure Stream Analytics) and a batch layer for comprehensive computations (e.g., Azure Data Lake). On the DP-900 exam, this question tests your understanding of how Azure services map to architectural patterns; a common trap is confusing Lambda with Kappa architecture, which uses only stream processing for both real-time and historical needs. Remember the memory tip: “Lambda has two lanes—speed for now, batch for then.”
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: lambda architecture combines batch and stream processing.. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Batch processing only would not support the real-time anomaly detection requirement.
B
Stream processing only
Why wrong: Stream processing only would not efficiently handle the large-scale historical batch analysis for trend identification.
C
Lambda architecture
Lambda architecture combines batch and stream processing, allowing both real-time anomaly detection and historical batch analysis on the same data set.
D
Data lake
Why wrong: 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.
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.
Key principle: Lambda architecture combines batch and stream processing.
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 not support the real-time anomaly detection requirement.
✗
Stream processing only
Why it's wrong here
Stream processing only would not efficiently handle the large-scale historical batch analysis for trend identification.
✓
Lambda architecture
Why this is correct
Lambda architecture combines batch and stream processing, allowing both real-time anomaly detection and historical batch analysis on the same data set.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Lambda architecture combines batch and stream processing.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, Lambda architecture uses a batch layer (e.g., Apache Hadoop, Azure Data Lake Storage) to compute immutable, comprehensive views from historical data, while the speed layer (e.g., Apache Storm, Azure Stream Analytics) processes real-time data streams with sub-second latency. The serving layer (e.g., Apache HBase, Azure Cosmos DB) merges results, handling late-arriving data by recomputing batch views. A real-world scenario is an IoT pipeline where sensor data is ingested via Azure IoT Hub, streamed through Azure Stream Analytics for immediate alerts, and stored in Azure Data Lake for nightly Spark jobs that detect long-term degradation trends.
KKey Concepts to Remember
Lambda architecture combines batch and stream processing.
It addresses both real-time and historical data analysis needs.
Comprises a speed layer for real-time and a batch layer for historical processing.
Often uses a data lake for raw data storage in the batch layer.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Lambda architecture combines batch and stream processing.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Lambda architecture combines batch and stream processing. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Lambda architecture combines batch and stream processing..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Review lambda architecture combines batch and stream processing., then practise related DP-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Lambda architecture combines batch and stream processing.
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