- A
CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize totalBytes = sum(BytesSent) by bin(TimeGenerated, 5m) | where totalBytes > 100000000
Option A correctly filters by source IP, aggregates total bytes sent in 5-minute windows using `summarize` with `bin`, and filters for windows exceeding 100 MB. This is the standard pattern for detecting large outbound transfers over time.
- B
CommonSecurityLog | where SourceIp == '10.0.0.1' | extend bin = bin(TimeGenerated, 5m) | where BytesSent > 100000000
Why wrong: Option B is incorrect because it filters individual events where BytesSent > 100 MB, rather than aggregating over a time window. Data exfiltration typically involves cumulative transfers over a period, not a single large event.
- C
CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize make_list(BytesSent) by TimeGenerated | where array_length(make_list) > 100000000
Why wrong: Option C is incorrect because it uses `make_list` to create a list of all BytesSent values per timestamp, then checks the length of the list, which counts the number of events, not the total bytes. This does not achieve the goal of summing bytes.
- D
CommonSecurityLog | where SourceIp == '10.0.0.1' | project BytesSent, TimeGenerated | summarize sum(BytesSent) by bin(TimeGenerated, 5m) | where sum_BytesSent > 100000000
Why wrong: Option D is syntactically valid; the `summarize` operator automatically creates a column named `sum_BytesSent`. However, the unnecessary `project` step makes it less efficient, and option A is preferred for clarity and best practice. The query would produce the same results as option A, but option A is the best answer.
KQL Summarize Operator with bin() for Time-Window Aggregation
This SC-200 practice question tests your understanding of mitigate threats using microsoft sentinel. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 threat hunter in Microsoft Sentinel writes a KQL query in the Logs blade to find possible data exfiltration. The query uses the CommonSecurityLog table to look for large outbound file transfers from a specific IP address. The analyst wants to include only events where the total bytes sent in a 5-minute window exceed 100 MB. Which KQL operator combination would best achieve this?
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
CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize totalBytes = sum(BytesSent) by bin(TimeGenerated, 5m) | where totalBytes > 100000000
Option A is correct because it first filters the CommonSecurityLog table for the specific source IP, then uses `summarize` with `bin(TimeGenerated, 5m)` to aggregate total bytes sent in 5-minute windows, and finally filters for windows where the sum exceeds 100 MB (100,000,000 bytes). This correctly implements a time-windowed aggregation to detect large outbound transfers, which is the standard pattern for identifying data exfiltration over a period.
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.
- ✓
CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize totalBytes = sum(BytesSent) by bin(TimeGenerated, 5m) | where totalBytes > 100000000
Why this is correct
Option A correctly filters by source IP, aggregates total bytes sent in 5-minute windows using `summarize` with `bin`, and filters for windows exceeding 100 MB. This is the standard pattern for detecting large outbound transfers over time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
CommonSecurityLog | where SourceIp == '10.0.0.1' | extend bin = bin(TimeGenerated, 5m) | where BytesSent > 100000000
Why it's wrong here
Option B is incorrect because it filters individual events where BytesSent > 100 MB, rather than aggregating over a time window. Data exfiltration typically involves cumulative transfers over a period, not a single large event.
- ✗
CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize make_list(BytesSent) by TimeGenerated | where array_length(make_list) > 100000000
Why it's wrong here
Option C is incorrect because it uses `make_list` to create a list of all BytesSent values per timestamp, then checks the length of the list, which counts the number of events, not the total bytes. This does not achieve the goal of summing bytes.
- ✗
CommonSecurityLog | where SourceIp == '10.0.0.1' | project BytesSent, TimeGenerated | summarize sum(BytesSent) by bin(TimeGenerated, 5m) | where sum_BytesSent > 100000000
Why it's wrong here
Option D is syntactically valid; the `summarize` operator automatically creates a column named `sum_BytesSent`. However, the unnecessary `project` step makes it less efficient, and option A is preferred for clarity and best practice. The query would produce the same results as option A, but option A is the best answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse filtering individual events (Option B) with aggregating over a time window (Option A), or they misuse list functions (Option C) instead of sum aggregation, failing to recognize that data exfiltration detection requires cumulative byte totals over a period, not per-event thresholds.
Detailed technical explanation
How to think about this question
The `bin()` function in KQL creates fixed-size time buckets by aligning timestamps to the bucket boundary (e.g., 5-minute intervals starting at 00:00), which is essential for accurate time-windowed aggregation. The `summarize` operator groups rows by the binned time and computes the sum of `BytesSent`, producing a single row per window. In real-world exfiltration detection, attackers often spread data across multiple small packets to evade threshold-based alerts, so summing over a window is critical — a single-packet threshold (as in Option B) would miss such activity. The 100 MB threshold (100,000,000 bytes) is a common baseline for large data transfers in enterprise environments.
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.
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FAQ
Questions learners often ask
What does this SC-200 question test?
Mitigate threats using Microsoft Sentinel — This question tests Mitigate threats using Microsoft Sentinel — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: CommonSecurityLog | where SourceIp == '10.0.0.1' | summarize totalBytes = sum(BytesSent) by bin(TimeGenerated, 5m) | where totalBytes > 100000000 — Option A is correct because it first filters the CommonSecurityLog table for the specific source IP, then uses `summarize` with `bin(TimeGenerated, 5m)` to aggregate total bytes sent in 5-minute windows, and finally filters for windows where the sum exceeds 100 MB (100,000,000 bytes). This correctly implements a time-windowed aggregation to detect large outbound transfers, which is the standard pattern for identifying data exfiltration over a period.
What should I do if I get this SC-200 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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Same concept, more angles
1 more ways this is tested on SC-200
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. A KQL query detects brute-force attempts by summarizing failed sign-ins by user, IP address, and five-minute time bins. Which operator is most appropriate for this aggregation?
hard- ✓ A.summarize.
- B.project-away.
- C.parse_json.
- D.extend.
Why A: The `summarize` operator is the correct choice because it groups rows by specified columns (user, IP address, and five-minute time bins) and applies an aggregation function (e.g., `count()`) to detect brute-force patterns. In KQL, `summarize` is the only operator that can create time-binned aggregations using the `bin()` function, which is essential for grouping failed sign-ins into fixed five-minute intervals. This directly supports the brute-force detection requirement by counting failed attempts per user/IP/time window.
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Last reviewed: Jun 11, 2026
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