- A
Identifying sign-ins from IP addresses listed in a custom threat intelligence watchlist.
Scheduled rules can reference watchlists in KQL queries to match sign-in IPs against threat intelligence.
- B
Detecting anomalous sign-in behavior based on user entity behavior.
Why wrong: Anomaly detection based on UEBA requires an Anomaly analytics rule, not a scheduled rule.
- C
Correlating Windows Security Events to detect brute-force attacks.
Scheduled rules can aggregate failed logon events (e.g., Event ID 4625) over time windows to identify brute-force patterns.
- D
Automatically blocking malicious IPs on a firewall.
Why wrong: Automated blocking requires a playbook triggered by an automation rule; scheduled rules generate alerts/incidents but do not execute actions themselves.
SC-200 Mitigate threats using Microsoft Sentinel Practice Question
This SC-200 practice question tests your understanding of mitigate threats using microsoft sentinel. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Which of the following detection scenarios can be implemented using a scheduled analytics rule in Microsoft Sentinel? (Select all that apply.) (Choose 2.)
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
Identifying sign-ins from IP addresses listed in a custom threat intelligence watchlist.
Option A is correct because scheduled analytics rules in Microsoft Sentinel can be configured to run queries at regular intervals, and these queries can reference watchlists, including custom threat intelligence watchlists. By querying sign-in logs and joining them with a watchlist of known malicious IPs, the rule can identify sign-ins from those IPs and generate alerts. This is a common pattern for leveraging external threat intelligence within Sentinel's detection capabilities.
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.
- ✓
Identifying sign-ins from IP addresses listed in a custom threat intelligence watchlist.
Why this is correct
Scheduled rules can reference watchlists in KQL queries to match sign-in IPs against threat intelligence.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Detecting anomalous sign-in behavior based on user entity behavior.
Why it's wrong here
Anomaly detection based on UEBA requires an Anomaly analytics rule, not a scheduled rule.
- ✓
Correlating Windows Security Events to detect brute-force attacks.
Why this is correct
Scheduled rules can aggregate failed logon events (e.g., Event ID 4625) over time windows to identify brute-force patterns.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Automatically blocking malicious IPs on a firewall.
Why it's wrong here
Automated blocking requires a playbook triggered by an automation rule; scheduled rules generate alerts/incidents but do not execute actions themselves.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse detection scenarios with response actions, or assume that all behavioral detection (like UEBA) can be done with scheduled rules, when in fact scheduled rules are only for static, query-based detection, not for machine learning or automated remediation.
Detailed technical explanation
How to think about this question
Scheduled analytics rules in Microsoft Sentinel use Kusto Query Language (KQL) queries that run on a defined schedule (e.g., every 5 minutes) and can aggregate data over a lookback period. They support watchlist lookups via the `_GetWatchlist()` function, which allows joining against static or dynamic lists of indicators. For brute-force detection (Option C), a scheduled rule can correlate multiple Windows Security Event ID 4625 (failed logon) events from the same source IP within a time window, using `summarize` and `count` to trigger an alert when a threshold is exceeded.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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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: Identifying sign-ins from IP addresses listed in a custom threat intelligence watchlist. — Option A is correct because scheduled analytics rules in Microsoft Sentinel can be configured to run queries at regular intervals, and these queries can reference watchlists, including custom threat intelligence watchlists. By querying sign-in logs and joining them with a watchlist of known malicious IPs, the rule can identify sign-ins from those IPs and generate alerts. This is a common pattern for leveraging external threat intelligence within Sentinel's detection capabilities.
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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Last reviewed: Jun 11, 2026
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