Azure Stream Analytics is the correct choice because it is designed for real-time stream processing with reference data join, enabling the logistics company to combine live GPS data from Azure Event Hubs with static route information from Azure SQL Database. This service executes SQL-like queries on streaming data, allowing you to detect delays and trigger rerouting actions with minimal latency. On the DP-900 exam, this scenario tests your understanding of Azure Stream Analytics as the dedicated solution for real-time analytics, often contrasting it with Azure Synapse Analytics (which is for batch and big data) or Azure Data Factory (for orchestration). A common trap is choosing Event Hubs alone, but remember Event Hubs is only the ingestion source, not the processing engine. Memory tip: think of Stream Analytics as the "live query engine" that joins the fast-moving stream with a static reference table, like a GPS map overlay updating in real time.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 logistics company needs to analyze GPS data from delivery trucks in real time to detect delays and reroute deliveries. The GPS data is streamed into Azure Event Hubs. They also need to combine this live data with static route information stored in Azure SQL Database. Which Azure service should they use for the real-time processing?
Why wrong: Azure Data Factory is an orchestration service for batch data movement and transformation, not real-time stream processing.
B
Azure Stream Analytics
Correct. Azure Stream Analytics is a real-time analytics service that can process streaming data from Event Hubs and combine it with reference data from SQL Database to detect patterns and trigger actions.
C
Azure Analysis Services
Why wrong: Azure Analysis Services is used for semantic modeling and offline analytics, not real-time stream processing.
D
Azure Synapse Pipelines
Why wrong: Azure Synapse Pipelines (powered by Azure Data Factory) are for batch orchestration and data movement, not real-time streaming.
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
Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, capable of ingesting data from Azure Event Hubs, applying SQL-like queries to detect delays, and joining live streams with static reference data from Azure SQL Database. This enables the logistics company to analyze GPS data as it arrives and trigger rerouting actions with minimal latency.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Data Factory
Why it's wrong here
Azure Data Factory is an orchestration service for batch data movement and transformation, not real-time stream processing.
✓
Azure Stream Analytics
Why this is correct
Correct. Azure Stream Analytics is a real-time analytics service that can process streaming data from Event Hubs and combine it with reference data from SQL Database to detect patterns and trigger actions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is used for semantic modeling and offline analytics, not real-time stream processing.
✗
Azure Synapse Pipelines
Why it's wrong here
Azure Synapse Pipelines (powered by Azure Data Factory) are for batch orchestration and data movement, not real-time streaming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Stream Analytics with Azure Data Factory or Synapse Pipelines, mistakenly thinking that any data integration or pipeline service can handle real-time streaming, when in fact those services are batch-oriented and lack the low-latency stream processing engine required for live GPS analysis.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a temporal windowing mechanism (e.g., tumbling, hopping, or sliding windows) to aggregate and analyze streaming data in near real-time, with a default event ordering tolerance of up to 21 days. It can perform a JOIN between the live Event Hubs stream and the static reference data from Azure SQL Database by loading the reference data into memory and refreshing it at configurable intervals (e.g., every 5 minutes), enabling low-latency decision-making for rerouting.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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 an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Stream Analytics — Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, capable of ingesting data from Azure Event Hubs, applying SQL-like queries to detect delays, and joining live streams with static reference data from Azure SQL Database. This enables the logistics company to analyze GPS data as it arrives and trigger rerouting actions with minimal latency.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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