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| Section | Weight | Objectives |
|---|---|---|
| Administration and Configuration | 20% | - Controls orchestration - System settings and maintenance - Policy creation and enforcement - User and role management |
| Access Governance and Risk Management | 15% | - Data access governance policies - Incident handling and remediation - Access review and entitlement analysis - Risk scoring and prioritization |
| Data Discovery and Classification | 25% | - Pattern matching and classification rules - Scanner and detector configuration - Compliance framework alignment - Data register and sensitive data mapping |
| DSPM Architecture and Deployment | 20% | - Installation and configuration - Deployment planning and requirements - Architecture overview and components - Integration with cloud and on-prem environments |
| Troubleshooting and Maintenance | 10% | - Backup and recovery procedures - Log analysis and error resolution - Upgrade and patch management - System health monitoring |
| Monitoring, Analytics, and Reporting | 10% | - Alert configuration and monitoring - Compliance reporting and audit logs - Dashboard creation and customization - Analytics and data visualization |
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27. Frage
Which of the following statements describe real-world application of pattern matching in Forcepoint DSPM?
Select two.
Antwort: A,D
Begründung:
The correct answers are B and C . Forcepoint DSPM pattern matching is used to identify specific information inside documents during scans. Forcepoint defines pattern matching as functionality that identifies "particular pieces of information in a document" by using regular expressions that match file content. A credit-card-like numeric string is a direct fit because it represents structured sensitive data that can be detected using a predictable format and then associated with compliance obligations such as PCI-related handling.
Option C is also valid because Forcepoint DSPM supports custom identification of sensitive business data, including intellectual property and trade secrets. A regular expression can be configured to detect proprietary product identifiers, engineering codes, project names, or other technical markers associated with confidential product development. Forcepoint's pattern terminology defines a Pattern as the RegEx plus the rules associated with detection, and allows classification, compliance, and distribution tags to be applied when the pattern is detected.
Options A , D , and E describe general regex use cases outside Forcepoint DSPM pattern matching. They focus on scripting, log parsing, and web-form validation rather than data discovery, classification, and governance. References/topics: Pattern Matching, RegEx, Classification Tags, Compliance Tags, Sensitive Data Discovery .
28. Frage
Which of the following is an option to aid the process of reviewing the incidents generated from triggered rules in Controls Orchestration?
Antwort: A
Begründung:
The correct answer is B . Forcepoint DSPM Controls Orchestration rules are built around query-driven rule logic. In the rule configuration workflow, administrators select a dataset such as Files , Trustees , or Agent Activities , then configure the rule condition using GQL syntax . This GQL condition defines which records match the rule and therefore which results appear as incidents when the rule is triggered. Forcepoint documentation states that Controls Orchestration rules identify data matching specific criteria and that the Condition field uses GQL syntax to filter the selected dataset.
When reviewing incidents, Forcepoint's Incidents page provides a top-down view of orchestration rules and available match results. Selecting an incident card opens a results preview, and choosing View in the Page displays those matched files in Enterprise Search , where additional review and action options are available.
Enterprise Search is specifically designed to narrow scanned-file results using GetVisibility Query Language
, and Forcepoint notes that GQL filtering helps reduce the result set to a more manageable subset for analysis and action.
Timestamp, Risk type, and keyword filtering may be useful in other contexts, but the documented advanced review mechanism for these results is GQL-based filtering. References/topics: Controls Orchestration, Incidents, Enterprise Search, GQL Filtering, Rule Match Review .
29. Frage
Match each export option with an appropriate use case:
Antwort:
Begründung:
Explanation:
Export a Dashboard in PDF format # For analysis by other users
Export a Dashboard in JSON format # To duplicate a dashboard in another Forcepoint DSPM deployment Export Enterprise Search results in CSV format # To import data into a spreadsheet or database Export Enterprise Search results in JSONL format # To import data into a SIEM solution Forcepoint DSPM uses different export formats for different operational purposes. A Dashboard PDF export is intended for human-readable sharing, review, and offline analysis. PDF is best when the dashboard needs to be distributed to managers, auditors, analysts, or department stakeholders who need a static visual report rather than a reusable configuration object. Forcepoint documents dashboard export capability as supporting PDF and JSON export from the dashboard interface.
A Dashboard JSON export preserves the dashboard definition rather than just the visual output. This makes JSON the correct format when the goal is to duplicate or migrate a dashboard into another Forcepoint DSPM deployment, because the dashboard structure, layout, panels, widgets, and configuration can be reused.
For Enterprise Search , CSV is the correct format for spreadsheet or database workflows. CSV is tabular, broadly compatible, and suited for Excel-style analysis, reporting, joins, and external data review. Forcepoint documents Enterprise Search export as producing CSV or JSON downloads of filtered data.
JSONL is most appropriate for SIEM ingestion because each line can represent an individual structured event or record, making it easier for log pipelines and event-processing systems to parse at scale. References/topics:
Analytics Export, Dashboard Export, Enterprise Search Export, CSV, JSON/JSONL, SIEM Integration
.
30. Frage
Provide the matching definitions for these Forcepoint DSPM Controls Orchestration terms:
Antwort:
Begründung:
Explanation:
Workflow Configuration Tool # Allows the creation of GQL syntax rules with conditions for organizational files and users.
Rule-Based Automation # Establish criteria to identify data policy violations, data handling irregularities, and other anomalies.
Notification System # Triggered rules can be configured to generate alerts and send them to webhooks to ensure prompt action.
Incident Management # Review events and manage rules through cards.
Controls Orchestration in Forcepoint DSPM is the policy automation layer used to convert data-risk criteria into reviewable operational events. The Workflow Configuration Tool is represented by the rule-building interface under Policy Center > Controls Orchestration , where administrators define datasets, ownership, rule conditions, and GQL logic for organizational files, trustees, or agent activities. Forcepoint states that rules are created to identify data matching specific criteria and are applied during scans of the selected dataset.
Rule-Based Automation describes the criteria-driven detection model. Administrators establish rules that identify policy violations, data-handling irregularities, or other anomalies that require attention. Notification System maps to the ability to configure triggered rules to send external notifications, including webhook- based alerts, when matching data is detected. Incident Management maps to the Incidents workflow:
Forcepoint states that each rule is automatically added as a card in the Incidents tab, and selecting the card takes the user back to the associated rule. References/topics: Policy Center, Controls Orchestration, GQL Conditions, Notifications, Webhooks, Incidents .
31. Frage
Which of the following is a key benefit of defining a custom data taxonomy?
Antwort: A
Begründung:
The correct answer is D. Enhances data security . In Forcepoint DSPM, taxonomy is the structured classification model used to assign sensitivity meaning to discovered data. Forcepoint describes taxonomy as a machine-learning-based structured classification system that assigns sensitivity levels such as Confidential , General Business , Public , and Highly-Confidential . This classification directly corresponds to the Classification column in scan results, allowing security and governance teams to understand how sensitive a file or data asset is after scanning.
A custom taxonomy improves security because it lets an organisation align DSPM classification labels with its own internal data-handling rules, security protocols, and business language. Forcepoint notes that taxonomy customization ensures data handling aligns with an organisation's security protocols, and that applied tags are visible in the Data Asset Inventory . In the administration taxonomy workflow, Forcepoint also explains that predefined AI Mesh tags can be supplemented with custom tags for pattern matching and detection rules, and those tags can be mapped to data-source taxonomies for label writeback.
The other options describe possible operational goals but not the primary purpose of taxonomy. Taxonomy is not an authentication mechanism, deduplication feature, or storage-reduction tool. References/topics:
Taxonomy, Classification Tags, AI Mesh, Pattern Matching, Data Asset Inventory, Compliance Hub .
32. Frage
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