The correct choice is the anomaly detection rule type. This rule type is purpose-built for Microsoft Sentinel to apply machine learning algorithms that analyze historical data, such as the AzureActivity table, to establish a baseline of normal behavior and then flag deviations like rare operations on Azure Key Vaults. Unlike scheduled rules that rely on static thresholds or query logic, anomaly detection uses time series analysis and ML models to automatically adapt to changing patterns, making it ideal for identifying unknown or subtle threats. On the SC-200 exam, this question tests your ability to distinguish between rule types based on their analytical method—a common trap is choosing a scheduled rule with a custom query, but the keyword “machine learning algorithm” points directly to anomaly detection. Remember the mnemonic “ML for ML” (Machine Learning for Anomaly Detection) to lock in the connection.
SC-200 Mitigate threats using Microsoft Sentinel Practice Question
This SC-200 practice question tests your understanding of mitigate threats using microsoft sentinel. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 is configuring a Microsoft Sentinel scheduled analytics rule to detect rare operations on Azure Key Vaults. The rule uses the AzureActivity table. The analyst wants to use a machine learning algorithm to identify anomalies based on historical activity patterns. Which analytics rule type should the analyst choose?
Why wrong: Scheduled rules use KQL queries with fixed thresholds and do not incorporate machine learning for anomaly detection.
B
Microsoft Security Incident (for using existing alert triggers)
Why wrong: Microsoft Security Incident rules create incidents based on alerts from other Microsoft security products, not on raw data with ML.
C
Anomaly detection
Anomaly detection rules apply machine learning models to identify deviations from historical patterns, making them suitable for detecting rare operations.
D
NRT (Near-Real-Time)
Why wrong: NRT rules run near real-time but are based on static query logic without ML.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Anomaly detection
The Anomaly Detection rule type in Microsoft Sentinel is specifically designed to use machine learning algorithms to identify unusual patterns in historical data. For detecting rare operations on Azure Key Vaults based on historical activity patterns in the AzureActivity table, this rule type automatically applies time series analysis and ML models to baseline normal behavior and flag deviations, making it the correct choice.
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.
✗
Scheduled
Why it's wrong here
Scheduled rules use KQL queries with fixed thresholds and do not incorporate machine learning for anomaly detection.
✗
Microsoft Security Incident (for using existing alert triggers)
Why it's wrong here
Microsoft Security Incident rules create incidents based on alerts from other Microsoft security products, not on raw data with ML.
✓
Anomaly detection
Why this is correct
Anomaly detection rules apply machine learning models to identify deviations from historical patterns, making them suitable for detecting rare operations.
Related concept
Read the scenario before looking for a memorised answer.
✗
NRT (Near-Real-Time)
Why it's wrong here
NRT rules run near real-time but are based on static query logic without ML.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Scheduled rules with Anomaly Detection rules because both can run on a schedule, but only Anomaly Detection rules incorporate built-in machine learning algorithms for dynamic baseline analysis.
Detailed technical explanation
How to think about this question
Under the hood, Anomaly Detection rules in Sentinel use a Seasonal-Trend Decomposition procedure based on LOESS (STL) combined with a machine learning model to analyze historical data and establish a dynamic baseline. The rule then compares incoming events against this baseline, flagging those that deviate beyond a statistically significant threshold. In practice, this is ideal for detecting rare operations like an unexpected Key Vault key deletion or access from an unusual IP, where a static threshold would either miss the anomaly or generate excessive false positives.
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: Anomaly detection — The Anomaly Detection rule type in Microsoft Sentinel is specifically designed to use machine learning algorithms to identify unusual patterns in historical data. For detecting rare operations on Azure Key Vaults based on historical activity patterns in the AzureActivity table, this rule type automatically applies time series analysis and ML models to baseline normal behavior and flag deviations, making it the correct choice.
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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