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MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company uses Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application uses event time and watermarks for windowed aggregations. The team notices that the output from tumbling windows is delayed, and many late records are being dropped. What is the MOST likely cause?

Clue words in this question

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

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

A
The checkpointing interval is too long, causing state to be lost
Why wrong: Checkpointing interval affects failure recovery, not late record handling.
B
The parallelism is too low, causing backpressure
Why wrong: Backpressure affects throughput but does not cause late records to be dropped.
C
The source is marking itself as idle, causing watermarks to stall
Why wrong: Idle sources can cause watermarks to not advance, but the symptom is delayed windows, not dropping late records.
D
The allowed lateness is set too low, causing late records to be discarded
Low allowed lateness means records arriving after the watermark are dropped.

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

✓

The allowed lateness is set too low, causing late records to be discarded

Option D is correct because the described symptoms—delayed output and dropped late records—are classic indicators that the `allowedLateness` parameter is set too low. In Apache Flink, event-time processing relies on watermarks to determine when a window is complete; if `allowedLateness` is too short, any record arriving after the watermark passes the window's end time is discarded as late. The team's observation that many late records are being dropped directly points to this configuration issue.

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.

✗
The checkpointing interval is too long, causing state to be lost
Why it's wrong here
Checkpointing interval affects failure recovery, not late record handling.
✗
The parallelism is too low, causing backpressure
Why it's wrong here
Backpressure affects throughput but does not cause late records to be dropped.
✗
The source is marking itself as idle, causing watermarks to stall
Why it's wrong here
Idle sources can cause watermarks to not advance, but the symptom is delayed windows, not dropping late records.
✓
The allowed lateness is set too low, causing late records to be discarded
Why this is correct
Low allowed lateness means records arriving after the watermark are dropped.
Clue confirmation
The clue word "most likely" 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

The trap here is that candidates confuse watermark stall (which delays output) with late record dropping—both involve watermarks, but stalled watermarks prevent window closure (no output), whereas low `allowedLateness` closes windows on time but discards subsequent late arrivals.

Detailed technical explanation

How to think about this question

Under the hood, Flink's `allowedLateness` defines how long after the watermark passes the window's end time the window will still accept late events (triggering an additional firing). Once the watermark exceeds the window end plus `allowedLateness`, the window is purged and any further late events are silently dropped. In real-world clickstream pipelines, network jitter or out-of-order data from mobile clients can cause significant lateness; setting `allowedLateness` too low (e.g., 0 seconds) will discard legitimate late arrivals, while a value like 5–10 minutes often balances completeness with state size.

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 MLS-C01 question test?

Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The allowed lateness is set too low, causing late records to be discarded — Option D is correct because the described symptoms—delayed output and dropped late records—are classic indicators that the `allowedLateness` parameter is set too low. In Apache Flink, event-time processing relies on watermarks to determine when a window is complete; if `allowedLateness` is too short, any record arriving after the watermark passes the window's end time is discarded as late. The team's observation that many late records are being dropped directly points to this configuration issue.

What should I do if I get this MLS-C01 question wrong?

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

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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