- A
Enable UEBA (User and Entity Behavior Analytics)
UEBA helps identify anomalies.
- B
Integrate Microsoft Defender XDR for cross-domain hunting
Defender XDR provides more data.
- C
Create custom hunting queries using KQL
Custom queries enable targeted hunting.
- D
Use watchlists to filter out known benign IPs
Why wrong: Watchlists are for correlation, not hunting.
- E
Reduce data retention period to improve query speed
Why wrong: Reduces data available for hunting.
Quick Answer
The answer is to create custom hunting queries using KQL, enable UEBA, and use the built-in hunting dashboards. These three actions directly improve threat hunting efficiency in Microsoft Sentinel because custom KQL queries allow you to hunt for specific IoCs and patterns, while UEBA leverages machine learning to baseline normal behavior and surface anomalies like impossible travel or unusual data exfiltration that static rules miss. On the Microsoft Cybersecurity Architect exam, this scenario tests your understanding of how to operationalize Sentinel’s hunting capabilities beyond simple alerting—a common trap is to overlook UEBA as a passive feature, but it actively enriches hunts by highlighting behavioral outliers. Memory tip: think “Query, Baseline, Dashboard” to recall the three pillars of efficient hunting.
SC-100 Practice Question: Design security operations, identity, and compliance capabilities
This SC-100 practice question tests your understanding of design security operations, identity, and compliance capabilities. 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 and wants to improve threat hunting efficiency. Which THREE actions should you take?
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
Enable UEBA (User and Entity Behavior Analytics)
UEBA (User and Entity Behavior Analytics) in Microsoft Sentinel uses machine learning models to establish baseline behavioral patterns for users, hosts, and other entities. It then detects anomalous activities such as unusual logon times, impossible travel, or abnormal data exfiltration, which directly enhances threat hunting by surfacing suspicious behaviors that might otherwise go unnoticed.
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.
- ✓
Enable UEBA (User and Entity Behavior Analytics)
Why this is correct
UEBA helps identify anomalies.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Integrate Microsoft Defender XDR for cross-domain hunting
Why this is correct
Defender XDR provides more data.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create custom hunting queries using KQL
Why this is correct
Custom queries enable targeted hunting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use watchlists to filter out known benign IPs
Why it's wrong here
Watchlists are for correlation, not hunting.
- ✗
Reduce data retention period to improve query speed
Why it's wrong here
Reduces data available for hunting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse passive data enrichment tools (like watchlists) with active hunting techniques, or mistakenly think reducing data retention improves security operations, when in fact it hinders long-term threat detection and forensic analysis.
Detailed technical explanation
How to think about this question
UEBA in Sentinel leverages Azure Machine Learning to create behavioral profiles using features like logon frequency, geolocation, and resource access patterns. It assigns an anomaly score (0-100) to each detected deviation, allowing hunters to prioritize high-scoring alerts. In a real-world scenario, UEBA can detect a compromised account that logs in from a new country within minutes of a previous legitimate login, even if the IP is not in any threat intelligence feed.
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.
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-100 question test?
Design security operations, identity, and compliance capabilities — This question tests Design security operations, identity, and compliance capabilities — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable UEBA (User and Entity Behavior Analytics) — UEBA (User and Entity Behavior Analytics) in Microsoft Sentinel uses machine learning models to establish baseline behavioral patterns for users, hosts, and other entities. It then detects anomalous activities such as unusual logon times, impossible travel, or abnormal data exfiltration, which directly enhances threat hunting by surfacing suspicious behaviors that might otherwise go unnoticed.
What should I do if I get this SC-100 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.
About these practice questions
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Last reviewed: Jun 24, 2026
This SC-100 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SC-100 exam.
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