Question 701 of 846

Develop data processing →mediumMultiple SelectObjective-mapped

Quick Answer

The two valid ways to handle late-arriving data in Azure Stream Analytics are configuring a late arrival tolerance window and using a temporal join to combine the late event with historical data. The late arrival tolerance window, which can be set up to a maximum of 21 days, allows the service to reorder events that arrive after their timestamp before processing, effectively accommodating delays in the stream. A temporal join, such as one using DATEDIFF, enables you to retroactively correct aggregations by linking the late-arriving event to the correct historical window or reference data. On the DP-203 exam, this topic tests your understanding of event ordering policies and time-based joins under the “Processing Time” and “Event Time” concepts. A common trap is confusing the late arrival window with the out-of-order window—remember that late arrival handles events arriving after their timestamp, while out-of-order handles events within the same window but with wrong sequence. Memory tip: “Late tolerance for tardy timestamps, temporal joins for retroactive fixes.”

DP-203 Develop data processing Practice Question

This DP-203 practice question tests your understanding of develop data processing. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.

Which TWO of the following are valid ways to handle late-arriving data in a streaming solution with Azure Stream Analytics? (Choose two.)

Question 1mediummulti select

Full question →

A
Reprocess the entire stream from the beginning when late data is detected.
Why wrong: Reprocessing is inefficient and not recommended; Stream Analytics handles late data within tolerance.
B
Implement a custom Azure Function as a 'LateDataHandler' in the query.
Why wrong: There is no built-in concept of a late data handler; custom functions are for complex logic, not for late arrival management.
C
Use a reference data input to store late-arriving events.
Why wrong: Reference data is static and not intended for streaming events.
D
Configure the 'late arrival tolerance' window in the event ordering settings up to 21 days.
Stream Analytics allows setting a late arrival tolerance window to handle events that arrive after the event time.
E
Use a temporal join to combine the late-arriving event with the historical window.
Temporal joins allow joining events from different time windows, enabling late data integration.

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

✓

Configure the 'late arrival tolerance' window in the event ordering settings up to 21 days.

Option D is correct because Azure Stream Analytics allows you to configure a 'late arrival tolerance' window in the event ordering settings, which can be set up to a maximum of 21 days. This window defines how long the service will wait to accommodate events that arrive after their timestamp, reordering them within that tolerance before processing. Option E is correct because a temporal join (e.g., using LATERAL or JOIN with DATEDIFF) can combine a late-arriving event with historical data from a reference or stream window, enabling you to retroactively correct aggregations or state.

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.

✗
Reprocess the entire stream from the beginning when late data is detected.
Why it's wrong here
Reprocessing is inefficient and not recommended; Stream Analytics handles late data within tolerance.
✗
Implement a custom Azure Function as a 'LateDataHandler' in the query.
Why it's wrong here
There is no built-in concept of a late data handler; custom functions are for complex logic, not for late arrival management.
✗
Use a reference data input to store late-arriving events.
Why it's wrong here
Reference data is static and not intended for streaming events.
✓
Configure the 'late arrival tolerance' window in the event ordering settings up to 21 days.
Why this is correct
Stream Analytics allows setting a late arrival tolerance window to handle events that arrive after the event time.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a temporal join to combine the late-arriving event with the historical window.
Why this is correct
Temporal joins allow joining events from different time windows, enabling late data integration.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse the 'late arrival tolerance' with a simple delay setting, not realizing it is a reordering buffer up to 21 days, and they overlook temporal joins as a valid pattern for handling late data, instead assuming only external functions or full reprocessing are options.

Detailed technical explanation

How to think about this question

The late arrival tolerance window works by buffering events and reordering them based on their timestamps, using the 'ArrivalTime' and 'EventTime' fields; events outside the tolerance are either dropped or assigned a new timestamp. Temporal joins in Stream Analytics use the DATEDIFF function to define a time window (e.g., 1 hour) to match late events with historical stream snapshots, enabling corrections like updating rolling averages. In real-world scenarios, IoT sensors with intermittent connectivity often send delayed telemetry, and combining a 21-day tolerance with a temporal join allows accurate retroactive adjustments without full reprocessing.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: Configure the 'late arrival tolerance' window in the event ordering settings up to 21 days. — Option D is correct because Azure Stream Analytics allows you to configure a 'late arrival tolerance' window in the event ordering settings, which can be set up to a maximum of 21 days. This window defines how long the service will wait to accommodate events that arrive after their timestamp, reordering them within that tolerance before processing. Option E is correct because a temporal join (e.g., using LATERAL or JOIN with DATEDIFF) can combine a late-arriving event with historical data from a reference or stream window, enabling you to retroactively correct aggregations or state.

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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