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.
Your company is developing a new analytics solution to track customer sentiment from social media feeds. The data arrives as a continuous stream of JSON messages. The solution must process the data in near real-time, enrich it with customer profile data stored in Azure Cosmos DB, and then store the results in a data lake for historical analysis. The team wants to use a low-code approach for the data processing logic. You are considering the following architectures: A) Use Azure Event Hubs to ingest the stream, Azure Stream Analytics to process and enrich the data using Cosmos DB as a reference data source, and output to Azure Data Lake Storage Gen2. B) Use Azure IoT Hub to ingest the stream, Azure Databricks to process the data, and write to Azure Blob Storage. C) Use Azure Event Hubs to ingest the stream, Azure Functions to process each message, query Cosmos DB for enrichment, and write to Azure Data Lake Storage Gen2. D) Use Azure Event Hubs to ingest the stream, Azure Data Factory to execute a mapping data flow for enrichment, and write to Azure Data Lake Storage Gen2. Which architecture best meets the requirements of near real-time processing, enrichment, and low-code?
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.
Option A
Stream Analytics offers low-code, near real-time, and supports reference data enrichment.
Option C
Why wrong: Azure Functions require code, not low-code.
Option D
Why wrong: Data Factory mapping data flows are better for batch, not near real-time.
Option B
Why wrong: Databricks is code-intensive, not low-code.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Option A
Option A is correct because Azure Stream Analytics provides a low-code, SQL-based approach for near real-time processing, and it can natively enrich streaming data by using Azure Cosmos DB as a reference data source via a JOIN operation. The output is directly written to Azure Data Lake Storage Gen2, meeting all requirements without custom code.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Option A
Why this is correct
Stream Analytics offers low-code, near real-time, and supports reference data enrichment.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Option C
Why it's wrong here
Azure Functions require code, not low-code.
Option D
Why it's wrong here
Data Factory mapping data flows are better for batch, not near real-time.
Option B
Why it's wrong here
Databricks is code-intensive, not low-code.
Common exam traps
The trap here is that candidates often confuse Azure Stream Analytics with Azure Data Factory, assuming both can handle streaming, but Data Factory is batch-only and cannot process a continuous Event Hubs stream in near real-time.
Detailed technical explanation
Azure Stream Analytics uses a temporal windowing mechanism (e.g., Tumbling, Hopping, Sliding windows) to process streaming data in near real-time, and its reference data JOIN allows enrichment by loading Cosmos DB data into memory as a static snapshot, refreshed periodically. Under the hood, Stream Analytics jobs are compiled into a directed acyclic graph (DAG) of operators that run on distributed compute nodes, ensuring low latency (sub-second to seconds) for high-throughput streams.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Option A — Option A is correct because Azure Stream Analytics provides a low-code, SQL-based approach for near real-time processing, and it can natively enrich streaming data by using Azure Cosmos DB as a reference data source via a JOIN operation. The output is directly written to Azure Data Lake Storage Gen2, meeting all requirements without custom code.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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