The answer is the `make_set()` function combined with the `lookup` operator. This is correct because `make_set()` builds a dynamic array of distinct country values per user over a historical time window, and `lookup` allows you to compare the current sign-in’s country against that stored set, flagging any login from a country not present in the array. On the SC-200 exam, this pattern tests your ability to implement user-based anomaly detection in Microsoft Sentinel using KQL, often appearing in scenarios where you must differentiate between first-seen and recurring behaviors. A common trap is confusing `make_set()` with `make_list()`, which includes duplicates and would break the uniqueness logic. For a memory tip, think “set for unique, list for repeat” — `make_set()` ensures each country appears only once, making the lookup for new countries both efficient and accurate.
SC-200 Mitigate threats using Microsoft Sentinel Practice Question
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 SOC team wants to use Microsoft Sentinel to detect when a user logs in from a new country not previously seen for that user. They have the SigninLogs table. Which KQL function is most appropriate to build this anomaly detection?
Why wrong: timechart() is used for visual time series, not for set operations.
B
make_set() with lookup
make_set() creates an array of previously seen countries per user. Using a join or lookup operation, you can compare a new login's country against that set to detect anomalies.
C
dcount()
Why wrong: dcount() returns an approximate distinct count, not the actual set of values needed for comparison.
D
startofday()
Why wrong: startofday() is a time function to round timestamps, not useful for set detection.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
make_set() with lookup
The `make_set()` function creates a dynamic array of distinct values (e.g., countries) per user over a specified time window. By using `lookup` to compare the current sign-in's country against the historical set, you can flag logins from countries not previously seen. This directly implements the 'new country' anomaly detection pattern in KQL.
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.
✗
timechart()
Why it's wrong here
timechart() is used for visual time series, not for set operations.
✓
make_set() with lookup
Why this is correct
make_set() creates an array of previously seen countries per user. Using a join or lookup operation, you can compare a new login's country against that set to detect anomalies.
Related concept
Read the scenario before looking for a memorised answer.
✗
dcount()
Why it's wrong here
dcount() returns an approximate distinct count, not the actual set of values needed for comparison.
✗
startofday()
Why it's wrong here
startofday() is a time function to round timestamps, not useful for set detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between aggregate functions that return counts (`dcount`) versus those that return the actual set of values (`make_set`), leading candidates to choose `dcount` when they need to compare individual values against a historical list.
Detailed technical explanation
How to think about this question
Under the hood, `make_set()` uses the `summarize` operator to aggregate values into a JSON array, which can be stored in a variable or materialized view for efficient lookup. The `lookup` operator then performs a left outer join against this historical set, allowing you to filter rows where the current country is not in the set. In real-world scenarios, you would also use `datetime` binning (e.g., `bin(TimeGenerated, 1d)`) to limit the historical window and avoid stale data, and you might combine this with `array_index_of()` for more granular checks.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this SC-200 question in full detail.
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: make_set() with lookup — The `make_set()` function creates a dynamic array of distinct values (e.g., countries) per user over a specified time window. By using `lookup` to compare the current sign-in's country against the historical set, you can flag logins from countries not previously seen. This directly implements the 'new country' anomaly detection pattern in KQL.
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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