Quick Answer
The answer is the `summarize` operator, which is the correct choice because it groups events by a specified field—here, the source IP address—and then uses an aggregation function like `dcount()` to count distinct user accounts from the failed logon attempts. After summarizing, a `where` clause filters for counts above 10, directly matching the detection rule’s threshold for a password spray attack. On the SC-200 exam, this pattern tests your ability to detect password spray with summarize dcount, a common scenario where attackers try a few passwords on many accounts from one IP. A frequent trap is confusing `summarize` with `project` or `extend`, which cannot aggregate; remember that any time you need to group and count distinct values, `summarize` is your go-to. Memory tip: “Summarize to size up the spray”—use it to group by IP and count distinct users, then filter the big numbers.
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 SOC analyst in Microsoft Sentinel is creating a scheduled analytics rule to detect a possible password spray attack. The rule must trigger when a single source IP address has more than 10 failed logon attempts on different user accounts within a 30-minute window. The analyst writes a KQL query starting with 'SigninLogs | where ResultType == 50057' (failed logon). Which operator should the analyst use to group events by source IP and count distinct user accounts, then filter for counts above 10?
summarize
The 'summarize' operator groups rows and applies aggregate functions like dcount() or count() to calculate distinct user counts per IP.
where
Why wrong: The 'where' operator filters rows but does not perform grouping or counting.
extend
Why wrong: The 'extend' operator adds new columns but does not reduce or aggregate rows.
project
Why wrong: The 'project' operator selects columns but cannot perform aggregation.
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 required to group events by source IP address and count distinct user accounts using `dcount()` or `count()`. After summarizing, you apply a `where` clause to filter for counts above 10, which meets the rule's threshold. This is the standard pattern for aggregation in KQL.
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
The 'summarize' operator groups rows and applies aggregate functions like dcount() or count() to calculate distinct user counts per IP.
Related concept
Read the scenario before looking for a memorised answer.
where
Why it's wrong here
The 'where' operator filters rows but does not perform grouping or counting.
extend
Why it's wrong here
The 'extend' operator adds new columns but does not reduce or aggregate rows.
project
Why it's wrong here
The 'project' operator selects columns but cannot perform aggregation.
Common exam traps
The trap here is that candidates often confuse `summarize` with `extend` or `project`, thinking they can achieve aggregation without an explicit grouping operator, or they mistakenly use `where` after a simple filter instead of performing the required count and threshold check.
Detailed technical explanation
The `summarize` operator in KQL groups rows by the specified key (source IP) and applies an aggregate function like `dcount(UserPrincipalName)` to count distinct users. Under the hood, this uses hash-based aggregation, which is efficient for large datasets. In a real-world password spray detection, you would also consider time-binning with `bin(TimeGenerated, 30m)` to ensure the count is within the 30-minute window.
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 required to group events by source IP address and count distinct user accounts using `dcount()` or `count()`. After summarizing, you apply a `where` clause to filter for counts above 10, which meets the rule's threshold. This is the standard pattern for aggregation in KQL.
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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Last reviewed: Jun 11, 2026
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