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SC-900 Practice Question: Describe the capabilities of Microsoft security solutions

Your organization uses Microsoft Sentinel. You need to create a custom analytics rule that triggers an incident when a user fails to sign in more than five times within an hour. Which rule type should you use?

⚠ Common exam trap

Test-takers frequently confuse 'near-real-time' with 'real-time aggregation over a window,' but NRT rules cannot perform time-based aggregation like counting events over an hour, which is a key differentiator from scheduled query rules.

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

Scheduled query rule

A scheduled query rule is the correct choice because it allows you to define a KQL query that counts sign-in failures per user over a specified time window (e.g., 1 hour) and triggers an incident when the count exceeds a threshold (e.g., 5). This rule type runs on a schedule you define, making it ideal for detecting patterns like brute-force attempts that require aggregation over time.

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 query rule

    Why this is correct

    This rule type is fundamental for proactive threat detection in Microsoft Sentinel, enabling security analysts to define and execute custom KQL queries against ingested log data on a recurring schedule. It allows for specifying a precise query interval and a lookback period, triggering an alert only when the query results meet a predefined numerical threshold. This makes it ideal for identifying specific patterns, event counts, or conditions over a chosen time window.

  • Fusion rule

    Why it's wrong here

    Fusion rules in Microsoft Sentinel are an advanced capability designed to automatically detect sophisticated multi-stage attacks by correlating disparate alerts from various security products. They leverage Microsoft's proprietary machine learning algorithms to identify complex threat chains that might otherwise go unnoticed. Crucially, these rules do not allow users to define custom KQL queries for direct log analysis or to set specific numerical thresholds for individual events.

  • Near-real-time (NRT) analytics rule

    Why it's wrong here

    Near-real-time (NRT) analytics rules are specifically engineered for high-velocity threat detection, allowing KQL queries to run as frequently as every minute for immediate response. While providing rapid incident identification, their design imposes a strict limitation on the query's lookback period, typically a maximum of 5 minutes. This constraint prevents NRT rules from performing aggregations or analyses over longer durations, such as an hour or more, making them unsuitable for scenarios requiring broader historical context.

  • Machine learning (ML) behavioral analytics rule

    Why it's wrong here

    Machine learning (ML) behavioral analytics rules in Microsoft Sentinel are dedicated to identifying anomalous user and entity behavior (UEBA) by establishing baselines of normal activity. These pre-built rules leverage sophisticated algorithms to detect deviations from these baselines, signaling potential compromises or insider threats. They are not designed for custom KQL query execution or for triggering alerts based on fixed, user-defined numerical thresholds for specific event occurrences.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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