Quick Answer
The answer is the `summarize` operator, which is the most appropriate choice for time-binned aggregation in KQL because it groups rows by specified columns—such as user, IP address, and five-minute time bins created with the `bin()` function—and then applies an aggregation function like `count()` to reveal patterns. This operator is essential for detecting brute-force attempts, as it efficiently counts failed sign-ins per user and IP address within fixed time windows, directly supporting the security detection requirement. On the Microsoft Security Operations Analyst SC-200 exam, this concept tests your ability to build KQL queries for threat hunting in Microsoft Sentinel, often appearing in scenarios where you must distinguish `summarize` from operators like `project` or `extend`, which cannot perform grouping or binning. A common trap is confusing `summarize` with `make-series`, but remember: `summarize` with `bin()` is your go-to for fixed-interval aggregation, while `make-series` creates time series with default fill. Memory tip: “Summarize bins the time, then counts the crime.”
This SC-200 practice question tests your understanding of mitigate threats using microsoft sentinel. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 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?
summarize.
summarize is used for aggregation such as count() by user, IP, and bin(TimeGenerated, 5m).
project-away.
Why wrong: project-away removes columns; it does not aggregate events.
parse_json.
Why wrong: parse_json converts JSON text to dynamic objects.
extend.
Why wrong: extend creates calculated columns but does not aggregate rows.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
summarize.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
summarize.
Why this is correct
summarize is used for aggregation such as count() by user, IP, and bin(TimeGenerated, 5m).
Related concept
Read the scenario before looking for a memorised answer.
project-away.
Why it's wrong here
project-away removes columns; it does not aggregate events.
parse_json.
Why it's wrong here
parse_json converts JSON text to dynamic objects.
extend.
Why it's wrong here
extend creates calculated columns but does not aggregate rows.
Common exam traps
The trap here is that candidates may confuse `extend` with `summarize` because both can create new columns, but only `summarize` performs grouping and aggregation, which is essential for detecting brute-force patterns over time bins.
Detailed technical explanation
The `summarize` operator in KQL works by partitioning the input table into groups based on the `by` clause (e.g., `by User, IP, bin(TimeGenerated, 5m)`) and then applying an aggregate function like `count()` to each group. The `bin()` function is critical here because it rounds timestamps to the nearest five-minute boundary, ensuring that events within the same window are grouped together. In a real-world brute-force detection scenario, you would combine `summarize` with `where ResultType == 'Failure'` and then filter for groups with a high count (e.g., `| where FailedAttempts > 10`) to trigger an alert in Microsoft Sentinel.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Mitigate threats using Microsoft Sentinel — This question tests Mitigate threats using Microsoft Sentinel — Read the scenario before looking for a memorised answer..
The correct answer is: summarize. — 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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 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?
hardWhy A: 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.
Last reviewed: Jun 11, 2026
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