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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Incident Detection, Investigation and Response | 15% | - Applying incident response workflows and escalation - Using dashboards and tools for incident investigation |
| Topic 2: Event Correlation and Rule Management | 20% | - Creating and configuring correlation rules - Managing alerts, tuning rules, reducing false positives |
| Topic 3: Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Topic 4: Monitoring, Reporting and Integration | 15% | - Integrating with security tools and ZTNA - Generating compliance and operational reports - Configuring dashboards and real-time monitoring |
| Topic 5: Analytics | 30% | - Performing CMDB and lookup table queries - Building queries from search results and events - Applying group by and data aggregation |
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NEW QUESTION # 59
How can you query the configuration management database (CMDB) in an analytics search?
Answer: D
Explanation:
In an analytics search, you can query the CMDB by clicking Value > Select from CMDB, which allows you to choose values directly from CMDB entries for the selected attribute, enabling precise filtering based on asset data.
NEW QUESTION # 60
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: B
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 # 61
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: C
NEW QUESTION # 62
You want to create a rule with multiple subpatterns but trigger an incident only if three different subpatterns are matched over a 24-hour period. Where must you define the time period that the rule uses to evaluate all the subpatterns?
Answer: C
Explanation:
For a rule that evaluates multiple subpatterns together, the shared evaluation period is configured as the rule's time window under the General tab. This defines the overall period FortiSIEM uses to correlate the required subpattern matches before triggering the incident.
NEW QUESTION # 63
Refer to the exhibit. What will happen when a device being analyzed by the machine learning (ML) configuration shown in the exhibit has a consistently high memory use?
Answer: A
Explanation:
The ML regression model uses memory utilization, sent bytes, and received bytes as prediction inputs for CPU utilization. If memory usage remains consistently high, FortiSIEM adapts the learned baseline and updates the model with a higher average memory utilization value over time.
NEW QUESTION # 64
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