Quick Answer
The answer is a scheduled query rule. This rule type is correct because it runs at defined intervals—such as every five minutes—and can aggregate log data to count events like failed sign-ins from a single IP address, then trigger an incident when a threshold (more than 10) is exceeded. On the SC-900 exam, this question tests your understanding of the core analytics rule types in Microsoft Sentinel, specifically distinguishing scheduled queries from near-real-time (NRT) rules, which lack robust aggregation, and from machine learning or fusion rules that rely on different detection methods. A common trap is choosing NRT because it sounds faster, but remember: scheduled queries are designed for threshold-based, time-window aggregations. Memory tip: “Schedule your thresholds” — if you need to count events over time, schedule it.
This SC-900 practice question tests your understanding of describe the capabilities of microsoft security solutions. 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.
Your organization uses Microsoft Sentinel. You need to create an analytics rule that triggers an incident when more than 10 failed sign-ins occur from a single IP address within 5 minutes. Which rule type should you use?
Fusion rule
Why wrong: Incorrect: Fusion correlates multiple alerts, not a single threshold condition.
Scheduled query rule
Correct: Scheduled rules allow aggregation (e.g., count>10) over time windows.
Near-real-time (NRT) rule
Why wrong: Incorrect: NRT rules have limited aggregation capabilities for complex thresholds.
ML Behavior Analytics rule
Why wrong: Incorrect: ML rules use anomaly detection, not fixed thresholds.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Scheduled query rule
Scheduled query rules run at intervals and can aggregate events. Option B is correct. Option A (NRT) provides near-real-time but limited aggregation. Option C (ML Behavior Analytics) uses ML. Option D (Fusion) correlates alerts.
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.
Fusion rule
Why it's wrong here
Incorrect: Fusion correlates multiple alerts, not a single threshold condition.
Scheduled query rule
Why this is correct
Correct: Scheduled rules allow aggregation (e.g., count>10) over time windows.
Related concept
Read the scenario before looking for a memorised answer.
Near-real-time (NRT) rule
Why it's wrong here
Incorrect: NRT rules have limited aggregation capabilities for complex thresholds.
ML Behavior Analytics rule
Why it's wrong here
Incorrect: ML rules use anomaly detection, not fixed thresholds.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which SC-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Describe the capabilities of Microsoft security solutions — This question tests Describe the capabilities of Microsoft security solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Scheduled query rule — Scheduled query rules run at intervals and can aggregate events. Option B is correct. Option A (NRT) provides near-real-time but limited aggregation. Option C (ML Behavior Analytics) uses ML. Option D (Fusion) correlates alerts.
Identify which SC-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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. Refer to the exhibit. You are creating a custom analytics rule in Microsoft Sentinel. What does this rule detect?
hardWhy D: The rule is configured with 'Risk level: Medium, High' and 'Location: Outside US'. This means it triggers only when both conditions are met: the sign-in risk is medium or high, and the location is outside the US. Option D correctly matches this combination, detecting sign-ins with medium or high risk from outside the US.
Variation 2. Refer to the exhibit. The KQL query is used in a Microsoft Sentinel analytics rule. What is the primary purpose of this rule?
hardWhy C: The query filters alerts for 'Sensitive file shared externally' and further refines to files with SensitivityLabel 'Highly Confidential'. It projects the file name and owner. Option C is correct. Option A is too broad (any shared file). Option B mentions 'all alerts'. Option D is incorrect because the query does not block sharing.
Variation 3. You are analyzing sign-in logs in Microsoft Sentinel. Based on the KQL query in the exhibit, what is the purpose of this query?
hardWhy A: Option B is correct because the query filters for result type 50057 (user account disabled) and counts attempts per user, then filters for more than 10 attempts. This could indicate a user trying to sign in with a disabled account. Option A is wrong because it filters for a specific error, not all attempts. Option C is wrong because it counts by user, not IP. Option D is wrong because it does not analyze impossible travel.
Variation 4. Refer to the exhibit. You are analyzing a Microsoft Sentinel workspace using KQL. The query returns no results, but you know that malware alerts have been generated today. What is the most likely reason?
hardWhy D: The query uses the table 'SecurityAlert', but in Microsoft Sentinel, alerts are stored in the 'SecurityAlert' table only from some sources. However, for analytics rule alerts, the data is in the 'SecurityIncident' table or the specific alert table. The most common issue is that the table name is incorrect; the correct table for alerts from analytics rules is 'SecurityAlert' but sometimes the data is in 'Alert' or the table name might be case-sensitive. However, a more common mistake is that the column name should be 'AlertSeverity' but it's correct. The likely issue is that the alerts are stored in a different table, such as 'SecurityIncident' or 'Syslog'. Option B is correct because the query filters by AlertName, but the actual column might be 'AlertName' or 'Title'. In Sentinel, the standard column is 'AlertName'. Option A is wrong because the time filter is valid. Option C is wrong because the column exists. Option D is wrong because the syntax is fine.
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Last reviewed: Jun 21, 2026
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