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| Section | Objectives |
|---|---|
| Monitoring, Analytics, and Reporting | - Scan progress and risk visibility - Dashboards and analytics widgets - Data lineage and compliance monitoring |
| Administration and User Management | - User federation and Active Directory integration - Role-based access control (RBAC) - User synchronization and management |
| Deployment and Installation | - System requirements and prerequisites - Initial setup and configuration - Standalone DSPM deployment workflow |
| Forcepoint DSPM Architecture and Overview | - DSPM system components and platform overview - Deployment models (cloud / standalone) |
| Data Sources and Integration | - Database discovery and structured data scanning - Configuring data sources - Streaming and event integration - SIEM and external integrations |
| Data Classification and Detection | - Taxonomy and labeling mechanisms - Detectors and detector groups - Pattern matching and classification models |
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質問 # 34
Which of the following is NOT a key use case for detectors in Forcepoint DSPM?
正解：D
解説：
The correct answer is A. Encrypting data transfers . Detectors in Forcepoint DSPM are classification and discovery components, not transport-security controls. A detector is used to analyze file content, file paths, attributes, keywords, phrases, regular expressions, and positive or negative match terms so the platform can identify and categorize data during scans. Forcepoint describes Content Detectors as tools that "analyze file content to detect and categorize" based on keywords, phrases, or patterns, making them directly relevant to finding sensitive information.
Detectors also support compliance and breach-prevention outcomes because their findings contribute to the broader DSPM visibility model: identifying where sensitive data exists, how it is classified, and where risk- reduction actions may be needed. Forcepoint describes DSPM as providing visibility and risk remediation across cloud and on-premises environments, and notes that AI technology combined with Detectors and Compliance Hub helps organizations protect sensitive information and maintain regulatory requirements.
Encryption of data transfers, however, is handled by transport protocols, connector configuration, network security controls, or platform security architecture-not by detector logic. Detectors can help discover sensitive data that may require protection, but they do not encrypt traffic. References/topics: Detectors, Content Detectors, AI Mesh, Sensitive Data Discovery, Compliance Hub, Risk Remediation .
質問 # 35
Which of the following statements is true about Forcepoint DSPM pattern matching?
正解：C
解説：
The correct answer is A . Forcepoint DSPM pattern matching is designed to let administrators define and tune custom detection logic for organisation-specific sensitive data. The documented pattern-matching function identifies particular information in documents by using Regular Expressions , and Forcepoint notes that pattern matching uses Golang RegEx syntax, which is separate from Detector syntax.
When a new pattern is added under Administration > Pattern Matching , the configuration includes Pattern Name , Regular Expression , enablement state, hidden RegEx handling, and tag assignments for Classification , Compliance , and Distribution . Forcepoint states that the Regular Expression is "the sequence to be matched," and that selected tags can override machine-learning model output during endpoint suggestions and file scans. This supports the principle that custom patterns can be adjusted so the RegEx better matches an organisation's data formats, identifiers, project codes, or regulatory artifacts.
The other statements do not match the documented pattern workflow. Country assignment is not presented as a core custom-pattern field. Export/import of data patterns is not part of the stated pattern-matching configuration flow. Default or preconfigured patterns are provided as built-in detection content and should not be treated as administrator-editable RegEx templates. References/topics: Pattern Matching, Add New Pattern, RegEx, Classification Tags, Compliance Tags, Distribution Tags .
質問 # 36
If LDAP searching and updating is set to READ_ONLY mode, which synchronization option becomes irrelevant?
正解：A
解説：
The correct answer is Sync Registrations . In Forcepoint DSPM, Active Directory users are imported through Keycloak User Federation . The Forcepoint DSPM administration workflow directs administrators to access Keycloak, select the gv realm, navigate to User Federation , and add an LDAP provider for AD integration.
In Keycloak LDAP configuration, READ_ONLY edit mode means Keycloak can read LDAP-backed user data, but it cannot modify mapped LDAP attributes such as username, email, first name, last name, or passwords. Keycloak's own administration guide states that READ_ONLY prevents user attribute changes and password updates, while WRITABLE mode is the mode that allows changes to be synchronized back to LDAP.
That makes Sync Registrations irrelevant in READ_ONLY mode because Sync Registrations is specifically used when newly created Keycloak users should also be added to LDAP. Since READ_ONLY mode prevents Keycloak from writing new or modified user objects back to LDAP, registration synchronization has no useful effect in that configuration. By contrast, Import users , Periodic full sync , and Periodic changed users sync remain relevant because they are read-oriented synchronization mechanisms that bring LDAP/AD identities into DSPM's Keycloak-backed identity layer. References/topics: Keycloak User Federation, LDAP Edit Mode, READ_ONLY Mode, AD Import, Synchronization Settings .
質問 # 37
Match each export option with an appropriate use case:
正解：
解説：
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
.
質問 # 38
When creating a Detector, which field allows you to exclude specific criteria from the search?
正解：B
解説：
The correct answer is A. Not contain . In Forcepoint DSPM, detector creation uses inclusion and exclusion logic to control what should trigger a detector match. The Contain field defines the positive match criteria:
the keywords, phrases, regular expressions, extensions, or path terms that the detector should search for. The Not Contain field provides the negative condition: terms or criteria that should be ignored even when the broader detector logic would otherwise match.
Forcepoint's DSPM documentation for Content Detectors states that if there are terms the detector should ignore, they are set in the Not Contain field. The same concept appears in Path Detectors , where administrators define the search method, populate Contain with triggering terms, and then use Not Contain to exclude terms from matching.
This matters operationally because detectors can otherwise generate excessive or misleading matches. For example, a detector looking for payroll-related content may include "salary" or "compensation" in Contain , while excluding harmless template, archive, or test terms in Not Contain . Not Add , Except , and Exclude are not the documented detector-configuration field names. References/topics: Administration > Detectors, Content Detectors, Path Detectors, Contain/Not Contain logic, AI Mesh detector contribution .
質問 # 39
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