Question 685 of 846

Develop data processing →easyMultiple ChoiceObjective-mapped

Quick Answer

The correct query uses `SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, TumblingWindow(minute, 1)`. This is because a tumbling window is a fixed, non-overlapping time interval that resets exactly at the end of each minute, making it the ideal choice when you need to compute the average temperature per device every minute without any overlap between windows. On the DP-203 exam, this question tests your understanding of windowing functions in Azure Stream Analytics, specifically the distinction between tumbling, hopping, sliding, and session windows. A common trap is confusing tumbling windows with hopping windows—remember that tumbling windows have no overlap and no gaps, while hopping windows can overlap. For the search intent "azure stream analytics tumbling window average temperature," the key is that `TIMESTAMP BY` ensures the event time is used correctly, and `TumblingWindow(minute, 1)` guarantees each device’s average is computed in distinct one-minute buckets. Memory tip: think of a tumbling window like a metronome—each tick is a clean, separate slice of time.

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 using Azure Stream Analytics. The input is an Azure Event Hubs stream with JSON data containing a 'timestamp' field. You need to output the average temperature per device every minute using a tumbling window. Which query should you use?

Question 1easymultiple choice

Full question →

A
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, SlidingWindow(minute, 1)
Why wrong: Sliding window emits results when events occur within a gap, not fixed intervals.
B
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, TumblingWindow(minute, 1)
Tumbling window of 1 minute produces non-overlapping windows.
C
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, SessionWindow(minute, 1, 1)
Why wrong: Session window groups based on inactivity gaps.
D
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, HopWindow(minute, 1, 1)
Why wrong: HopWindow with equal size and hop is same as tumbling, but the question asks for tumbling.

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

✓

SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, TumblingWindow(minute, 1)

Option B is correct because a tumbling window is a fixed, non-overlapping time window that groups events into distinct time segments. Using `TumblingWindow(minute, 1)` with `TIMESTAMP BY Timestamp` ensures that the average temperature per device is computed over each one-minute interval without overlap, which matches the requirement of 'every minute'.

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.

✗
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, SlidingWindow(minute, 1)
Why it's wrong here
Sliding window emits results when events occur within a gap, not fixed intervals.
✓
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, TumblingWindow(minute, 1)
Why this is correct
Tumbling window of 1 minute produces non-overlapping windows.
Related concept
Read the scenario before looking for a memorised answer.
✗
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, SessionWindow(minute, 1, 1)
Why it's wrong here
Session window groups based on inactivity gaps.
✗
SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, HopWindow(minute, 1, 1)
Why it's wrong here
HopWindow with equal size and hop is same as tumbling, but the question asks for tumbling.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse `SlidingWindow` or `HopWindow` with `TumblingWindow`, not realizing that only `TumblingWindow` produces non-overlapping, fixed-interval outputs required for a simple per-minute average.

Detailed technical explanation

How to think about this question

Under the hood, Azure Stream Analytics uses the `TIMESTAMP BY` clause to define the event time for windowing operations, which is critical for handling out-of-order events via a late arrival policy. Tumbling windows are the simplest window type, partitioning the stream into fixed-size, non-overlapping intervals, and are ideal for periodic reporting like per-minute averages. In real-world scenarios, you must also consider the `Lateness` and `OutOfOrder` policies to handle delayed data without skewing results.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

What to study next

Got this wrong? Here's your next step.

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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: SELECT DeviceId, AVG(Temperature) AS AvgTemp FROM Input TIMESTAMP BY Timestamp GROUP BY DeviceId, TumblingWindow(minute, 1) — Option B is correct because a tumbling window is a fixed, non-overlapping time window that groups events into distinct time segments. Using `TumblingWindow(minute, 1)` with `TIMESTAMP BY Timestamp` ensures that the average temperature per device is computed over each one-minute interval without overlap, which matches the requirement of 'every minute'.

What should I do if I get this DP-203 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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