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Fortinet NSE6_FSM_AN-7.4 practice test has real Fortinet NSE 6 - FortiSIEM 7.4 Analyst (NSE6_FSM_AN-7.4) exam questions. You can change the difficulty of these questions, which will help you determine what areas appertain to more study before taking your Fortinet NSE 6 - FortiSIEM 7.4 Analyst (NSE6_FSM_AN-7.4) exam dumps. Here we listed some of the most important benefits you can get from using our Fortinet NSE6_FSM_AN-7.4 practice questions.
| Section | Objectives |
|---|---|
| Topic 1: Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| Topic 2: FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Topic 3: Rules and Incident Management | - Incidents and Notifications
|
| Topic 4: Analytics and Search | - Query and Event Analysis
|
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NEW QUESTION # 46
You need a model that predicts a target field based on other fields in a dataset and then triggers an anomaly if the value does not match the prediction. Which machine learning (ML) algorithm will you use to build this type of model?
Answer: C
NEW QUESTION # 47
Which analytics search can be used to apply a user and entity behavior analytics (UEBA) tag to an event for a failed login by the user JSmith?
Answer: B
Explanation:
The best matching analytics search is Username CONTAIN smit. The FortiSIEM Analytics lesson explains keyword and attribute matching by using the CONTAIN operator for searching values inside raw logs or attributes. The Study Guide gives a direct example of FortiSIEM translating keyword logic into a condition such as "Raw Event Log CONTAIN TCP." That shows the operator is designed for substring-style matching rather than full-value equality. For a user such as JSmith, the string fragment smit is contained inside the username value, so the condition can match events where the username appears as JSmith, jsmith, or possibly in a longer identity string such as a domain-qualified username.
Option A is wrong because smith is not the complete username and uses an equality-style comparison.
Option B is wrong because it excludes usernames ending in jsmith. Option C uses the wrong operator form for this context. Therefore, the practical FortiSIEM analytics condition that can match the target failed-login event is Username CONTAIN smit.
NEW QUESTION # 48
You need to model for predicting a target based on other fields in the dataset and then trigger an anomaly if the value does not match the prediction.
Which machine learning algorithm will build this type of model?
Answer: C
Explanation:
The correct answer is C. Regression. Regression is the machine learning task used when a model predicts a target value based on other fields in a dataset. In FortiSIEM's machine learning workflow, regression configuration includes selecting Fields to use for Prediction and a Field to Predict. The User Guide explains that during regression training, the analyst chooses the fields used for prediction and the field being predicted, then trains the model with a Train factor. This matches the question exactly:
the model predicts a target based on other fields, and the inference phase can identify anomalies when observed values differ significantly from predicted values. Classification is used to assign records to discrete classes or categories, not to predict a continuous target metric from other variables. Clustering groups similar observations without a predefined target field. Forecasting predicts future values over time, normally based on time-series behavior. Because the question specifically describes predicting a target field from other dataset fields and detecting mismatch from the prediction, the correct machine learning method is Regression.
NEW QUESTION # 49
What are the four incident status values on FortiSIEM?
Answer: B
Explanation:
FortiSIEM incidents can have four status values: Active, Auto Cleared, Cleared Manually, and System Cleared. These statuses track the lifecycle of an incident-from detection (Active) to resolution - whether it's cleared automatically by correlation logic or manually by an analyst.
NEW QUESTION # 50
Refer to the exhibit.
How was this incident cleared?
Answer: C
Explanation:
The Incident Status shows " Auto Cleared " , and the Cleared Reason states: " Rule has not been triggered for
20 minutes. " This indicates that the incident was automatically cleared by the rule logic after a defined period of inactivity.
The correct answer is C because the exhibit shows the incident status as Auto Cleared and the cleared reason indicates that the rule condition was no longer being triggered. The Study Guide explains that FortiSIEM supports clear conditions and auto-clearing behavior at the rule level. It states that if a time-based clear condition is configured, FortiSIEM can auto-clear the incident after the last occurrence if the trigger condition no longer exists. It also explains pattern-based clear behavior: FortiSIEM evaluates clear-condition subpatterns and compares attributes from the clear condition with the original incident attributes. If the configured attributes match, the incident status is set to auto cleared. In the exhibit, the cleared reason says the rule has not been triggered for a defined number of minutes. That is not a manual action by the analyst and not an endpoint-generated all-clear signal. It is FortiSIEM's rule-based clearing logic. Option B is also wrong because the exhibit shows a specific rule inactivity period, not a generic 24-hour timeout.
NEW QUESTION # 51
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