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| Section | Objectives |
|---|---|
| Topic 1: FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Topic 2: Rules and Incident Management | - Rules and Alerts
|
| Topic 3: Analytics and Search | - Query and Event Analysis
|
| Topic 4: Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
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NEW QUESTION # 34
Refer to the exhibit.
Which two conditions will match this rule and subpatterns? (Choose two.)
Answer: C,D
Explanation:
The user initiates an RDP session (Subpattern 1) and then fails to log in multiple times (Subpattern 2 with COUNT(Matched Events) > = 3) - both from the same Source IP and User within 300 seconds.
The brute force attempts typically involve a successful RDP connection followed by multiple failed logins, satisfying the sequence and grouping conditions in the rule.
The correct answers are A and B because the rule uses multiple subpatterns and requires them to occur in a defined relationship within the configured time window. The FortiSIEM Study Guide states that rule conditions specify the event attributes and thresholds that trigger the rule and create an incident. It also explains that the time window is the period "within which the subpattern(s) must match for the rule condition to be satisfied," and that when there is more than one subpattern, FortiSIEM requires logic between the subpatterns plus subpattern relationship constraints. In the exhibit, the first subpattern detects an RDP connection and the second detects failed logons. The rule condition uses a sequence relationship, so the failed logon activity must follow the RDP connection and match the relationship constraints, such as same user and source IP. A user using RDP over SSL VPN who fails repeatedly satisfies this logic. A brute-force attack against an RDP server also satisfies the repeated failed-logon requirement after an RDP connection. Failing only twice does not meet the aggregate threshold, and connecting to the wrong IP is not the failed-logon sequence being detected.
NEW QUESTION # 35
Refer to the exhibit.
If you group the events by User and Count attributes, how many results will FortiSIEM display?
Answer: A
Explanation:
The verified answer is D. Five . FortiSIEM grouping is based on unique combinations of the selected Group By fields. The Study Guide explains this behavior clearly: if multiple events have the same selected Group By values, "they are grouped together in one row," and the count column tracks the number of events for each row. In this question, the selected fields are User and Count . The six raw rows contain these combinations:
Mike/4, Bob/3, Alice/2, Alice/2, Bob/6, and Mike/5. Because Alice/2 appears twice, those two rows are grouped into a single result. The remaining combinations are unique. So FortiSIEM displays five grouped results, not six. Six would be correct only if every row had a unique User-and-Count combination, or if grouping included another differentiating attribute such as Source IP. Since the question specifically groups only by User and Count, duplicate User/Count pairs collapse into one row. Therefore, the correct result count is five .
NEW QUESTION # 36
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 # 37
Refer to the exhibit.
Which statement about the time range settings defined in the nested query is accurate? (Choose one answer)
Answer: D
Explanation:
The correct answer is D. The exhibit shows an outer event query using the Event Attribute filter Source IP NOT IN Device IP: Approved Devices. The outer query time range is set to Relative - Last 10 Minutes, so FortiSIEM searches only the event data from the last 10 minutes. The exhibit also shows a separate Nested Time Range set to Relative - Last 30 Days. In FortiSIEM nested searches, the nested time range applies to the inner report/subquery, not to the outer event search. The FortiSIEM 7.4 User Guide states that nested query functionality lets one query refer to results from another query, and for outer event / inner event nested searches, it instructs the user to "choose the time range for outer query" and separately "choose Nested Time Range for the inner query." It also states that when an existing query is used as an inner query, "time range would be set separately" in the outer query configuration. Therefore, FortiSIEM searches the last 10 minutes of outer events and compares their Source IP values against the Device IP values returned by the Approved Devices report using the last
30 days nested time range.
NEW QUESTION # 38
What is one difference between a lookup table and a watchlist?
Answer: D
Explanation:
A lookup table supports multiple columns and structured datasets, allowing more complex mappings and queries. A watchlist contains only a single column of values used for direct matching operations.
NEW QUESTION # 39
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