Question 629 of 846

Develop data processing →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to create an Azure Stream Analytics job with a Tumbling window of 5 minutes to compute the average price per symbol, then add conditional logic to detect price deviations exceeding 10% and output results to both Cosmos DB and Event Grid. This solution is correct because Azure Stream Analytics natively supports stateful, high-throughput stream processing with windowed aggregations like Tumbling windows, which partition data by Symbol and maintain state across multiple Event Hubs partitions without manual management. On the DP-203 exam, this scenario tests your understanding of real-time processing patterns versus batch-oriented tools; a common trap is choosing Azure Functions for high-velocity streams, but Functions lack built-in windowing and state management for 50,000 events per second. Remember the memory tip: "Tumble, compare, dual output" — Tumbling window for aggregation, compare for anomaly detection, and dual output to Cosmos DB and Event Grid for low-latency queries and alerts.

DP-203 Develop data processing Practice Question

This DP-203 practice question tests your understanding of develop data processing. 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.

You are developing a real-time data processing solution for a financial services company. The system ingests stock trade data from Azure Event Hubs at 50,000 events per second. Each event is a JSON object with fields: TradeID, Symbol, Price, Quantity, Timestamp. You need to calculate a 5-minute rolling average of the trade price per symbol and store the result in Azure Cosmos DB for low-latency queries. Additionally, you need to detect anomalies where the price deviates more than 10% from the rolling average within the same window, and send alerts to Azure Event Grid. You must minimize latency and ensure that the processing is stateful across multiple partitions. What should you do?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1hardmultiple choice

Full question →

A
Use Azure Functions with Event Hubs trigger. In each function invocation, compute the rolling average using a distributed cache (Redis) and detect anomalies. Write to Cosmos DB and Event Grid via output bindings.
Why wrong: Azure Functions may struggle with high throughput and state management across partitions.
B
Use Azure Synapse Pipelines with a Data Flow. Set up a streaming Data Flow from Event Hubs, compute rolling average using window functions, and sink to Cosmos DB and Event Grid.
Why wrong: Synapse Pipelines are batch-oriented; streaming Data Flows are in preview and less mature.
C
Use Azure Databricks with Structured Streaming. Read from Event Hubs using Kafka API. Perform windowed aggregations and anomaly detection using Spark SQL. Write to Cosmos DB via the Azure Cosmos DB Spark connector and to Event Grid via HTTP sink.
Why wrong: Databricks requires cluster management and is more complex to set up.
D
Create an Azure Stream Analytics job. Define input from Event Hubs. Use a Tumbling window of 5 minutes to compute average price per symbol. Add a custom function to compare each event's price to the average and output anomalies. Write to Cosmos DB via the Azure Cosmos DB output adapter and to Event Grid via the Event Grid output adapter.
Stream Analytics provides native support for windowing, stateful processing, and multiple outputs.

Full breakdown with real-world context →

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

✓

Create an Azure Stream Analytics job. Define input from Event Hubs. Use a Tumbling window of 5 minutes to compute average price per symbol. Add a custom function to compare each event's price to the average and output anomalies. Write to Cosmos DB via the Azure Cosmos DB output adapter and to Event Grid via the Event Grid output adapter.

Azure Stream Analytics can ingest from Event Hubs, perform windowed aggregations (e.g., Tumbling window for rolling average), detect anomalies using conditional logic, and output to both Cosmos DB and Event Grid. It handles partitioning automatically and is stateful. Option A is correct. Option B uses Azure Functions which are not ideal for high-throughput stateful stream processing. Option C uses Databricks Structured Streaming which is more complex to manage. Option D uses Synapse Pipelines which are batch-oriented.

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.

✗
Use Azure Functions with Event Hubs trigger. In each function invocation, compute the rolling average using a distributed cache (Redis) and detect anomalies. Write to Cosmos DB and Event Grid via output bindings.
Why it's wrong here
Azure Functions may struggle with high throughput and state management across partitions.
✗
Use Azure Synapse Pipelines with a Data Flow. Set up a streaming Data Flow from Event Hubs, compute rolling average using window functions, and sink to Cosmos DB and Event Grid.
Why it's wrong here
Synapse Pipelines are batch-oriented; streaming Data Flows are in preview and less mature.
✗
Use Azure Databricks with Structured Streaming. Read from Event Hubs using Kafka API. Perform windowed aggregations and anomaly detection using Spark SQL. Write to Cosmos DB via the Azure Cosmos DB Spark connector and to Event Grid via HTTP sink.
Why it's wrong here
Databricks requires cluster management and is more complex to set up.
✓
Create an Azure Stream Analytics job. Define input from Event Hubs. Use a Tumbling window of 5 minutes to compute average price per symbol. Add a custom function to compare each event's price to the average and output anomalies. Write to Cosmos DB via the Azure Cosmos DB output adapter and to Event Grid via the Event Grid output adapter.
Why this is correct
Stream Analytics provides native support for windowing, stateful processing, and multiple outputs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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.

What to study next

Got this wrong? Here's your next step.

Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

Related DP-203 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Secure, monitor, and optimize data storage and data processing practice questions

Practise DP-203 questions linked to Secure, monitor, and optimize data storage and data processing.

Design and develop data processing practice questions

Practise DP-203 questions linked to Design and develop data processing.

Design and implement data security practice questions

Practise DP-203 questions linked to Design and implement data security.

Monitor and optimize data storage and processing practice questions

Practise DP-203 questions linked to Monitor and optimize data storage and processing.

Design and implement data storage practice questions

Practise DP-203 questions linked to Design and implement data storage.

Develop data processing practice questions

Practise DP-203 questions linked to Develop data processing.

DP-203 fundamentals practice questions

Practise DP-203 questions linked to DP-203 fundamentals.

DP-203 scenario practice questions

Practise DP-203 questions linked to DP-203 scenario.

DP-203 troubleshooting practice questions

Practise DP-203 questions linked to DP-203 troubleshooting.

Practice this exam

Start a free DP-203 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

DP-203 practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this DP-203 question test?

Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Create an Azure Stream Analytics job. Define input from Event Hubs. Use a Tumbling window of 5 minutes to compute average price per symbol. Add a custom function to compare each event's price to the average and output anomalies. Write to Cosmos DB via the Azure Cosmos DB output adapter and to Event Grid via the Event Grid output adapter. — Azure Stream Analytics can ingest from Event Hubs, perform windowed aggregations (e.g., Tumbling window for rolling average), detect anomalies using conditional logic, and output to both Cosmos DB and Event Grid. It handles partitioning automatically and is stateful. Option A is correct. Option B uses Azure Functions which are not ideal for high-throughput stateful stream processing. Option C uses Databricks Structured Streaming which is more complex to manage. Option D uses Synapse Pipelines which are batch-oriented.

What should I do if I get this DP-203 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Same concept, more angles

1 more ways this is tested on DP-203

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. You are designing a near-real-time data processing solution that ingests millions of events per second from IoT devices. The data must be aggregated on a per-minute basis and stored in Azure Data Lake Storage Gen2 for long-term analytics. The solution must also support alerting when certain thresholds are exceeded. Which combination of Azure services should you use?

hard

A.Azure Event Hubs, Azure Data Factory, and Azure SQL Database.
✓ B.Azure IoT Hub, Azure Stream Analytics, and Azure Functions.
C.Azure IoT Hub, Azure Databricks with Structured Streaming, and Azure Data Lake Storage Gen2.
D.Azure Event Hubs, Azure Data Explorer, and Power BI.

Why B: Option D is correct because Azure IoT Hub ingests device data, Azure Stream Analytics performs real-time aggregation and alerting, and Azure Functions can write aggregated results to Data Lake Storage. Option A (Azure Event Hubs) is a generic event broker but lacks device management capabilities. Option B (Azure Databricks) is more suited for complex analytics than simple aggregation. Option C (Azure Data Explorer) is for ad-hoc analytics, not for streaming aggregation with alerting.

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.