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 manufacturing company ingests real-time sensor data from assembly line machines into Azure Event Hubs. The company needs to calculate a 5-minute rolling average of temperature readings for each machine and compare it against a static threshold value stored in a CSV file in Azure Blob Storage. If the average exceeds the threshold, an alert must be triggered. Which Azure service should be used for this real-time data processing?
Correct. Stream Analytics can process streaming data with window functions and join with reference data from Blob Storage.
B
Azure Data Factory
Why wrong: Incorrect. Data Factory is primarily for batch data integration and orchestration, not real-time stream processing.
C
Azure Synapse Analytics
Why wrong: Incorrect. Synapse Analytics is a data warehouse and analytics platform for large-scale batch and interactive queries, not optimized for continuous stream processing.
D
Azure HDInsight
Why wrong: Incorrect. HDInsight is a managed Hadoop/Spark cluster; it can do stream processing but requires cluster management and is more complex than Stream Analytics for this specific use case.
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, including windowed aggregations like a 5-minute rolling average. It can directly ingest data from Azure Event Hubs, perform the calculation using a TumblingWindow or HoppingWindow function, and reference static data (the threshold CSV) from Azure Blob Storage via a reference data input. If the computed average exceeds the threshold, Stream Analytics can output the alert to a sink like Azure Functions or a notification service.
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 Stream Analytics
Why this is correct
Correct. Stream Analytics can process streaming data with window functions and join with reference data from Blob Storage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
Incorrect. Data Factory is primarily for batch data integration and orchestration, not real-time stream processing.
✗
Azure Synapse Analytics
Why it's wrong here
Incorrect. Synapse Analytics is a data warehouse and analytics platform for large-scale batch and interactive queries, not optimized for continuous stream processing.
✗
Azure HDInsight
Why it's wrong here
Incorrect. HDInsight is a managed Hadoop/Spark cluster; it can do stream processing but requires cluster management and is more complex than Stream Analytics for this specific use case.
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 Analytics, mistakenly thinking that any data processing involving Blob Storage or SQL-like queries must use a batch-oriented service, when in fact Stream Analytics is the only option that natively supports real-time windowed aggregations and reference data joins from Blob Storage.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a temporal query language based on T-SQL with extensions like HoppingWindow (for overlapping windows) or TumblingWindow (for non-overlapping windows) to compute the rolling average. The reference data from Blob Storage is loaded into memory and joined with the event stream at query execution time, allowing the threshold comparison to occur without external lookups. In a real-world scenario, if the CSV file is updated, Stream Analytics automatically refreshes the reference data based on a configurable refresh interval (e.g., every 5 minutes), ensuring the threshold remains current without restarting the job.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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, including windowed aggregations like a 5-minute rolling average. It can directly ingest data from Azure Event Hubs, perform the calculation using a TumblingWindow or HoppingWindow function, and reference static data (the threshold CSV) from Azure Blob Storage via a reference data input. If the computed average exceeds the threshold, Stream Analytics can output the alert to a sink like Azure Functions or a notification service.
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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