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| Section | Objectives |
|---|---|
| Data Discovery and Scanning | - SMB Data Sources - Data Visibility and Monitoring - Scan Configuration - LDAP Integration |
| Preparing and Deploying Forcepoint DSPM | - Lab Environment Preparation - Data Source Configuration - DSPM Deployment Architecture - User Management Configuration |
| Detector Configuration | - Detector Testing and Validation - Keyword Detectors - Regular Expression Detectors - Path Detectors |
| Analytics and Dashboards | - Analytic Dashboards - Risk Analysis and Reporting - Enterprise Search - Dashboard Customization |
| Pattern Matching and Classification | - Regular Expressions - Custom Pattern Creation - Sensitive Data Identification - Classification Policies |
| Controls Orchestration | - Incident Investigation - Webhook Configuration - Rule Creation and Management - Risk Classification |
| Compliance Hub and Data Register | - Data Asset Inventory - Metadata Management - Security Posture Policies - Departments Configuration |
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NEW QUESTION # 27
Match the type of detector with its primary function.
Answer:
Explanation:
Explanation:
Path Detectors = Monitor storage locations of sensitive data
Content Detectors = Analyze data within files for sensitive data
Attribute Detectors = Identify meta-data within documents
Forcepoint DSPM separates detector logic into path, content, and attribute-based functions so classification can be driven by where data is stored, what the file contains, and what scan-derived attributes describe the file. Path Detectors evaluate file path information, including directory location, filename, and extension.
Forcepoint describes them as identifying and classifying files based on directory location and file extensions, which aligns with monitoring storage locations where sensitive data resides.
Content Detectors inspect the actual contents of files. Forcepoint states that content detectors analyze file content and categorize based on keywords, phrases, positive and negative terms, or patterns, making them the correct match for analyzing data within files for sensitive data.
Attribute Detectors operate on scan-result attributes rather than raw text content alone. Forcepoint describes them as defining criteria for "critical" and "sensitive" based on attributes in scan results, including ML output or path/content detector hits. This maps most closely to identifying metadata or scan-derived characteristics within documents. References/topics: Detectors, Path Detectors, Content Detectors, Attribute Detectors, AI Mesh, Classification Model .
NEW QUESTION # 28
Match each export option with an appropriate use case:
Answer:
Explanation:
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
.
NEW QUESTION # 29
Anna created a complex GQL query that she plans on using weekly to filter the results and export the data for review. How can she store this query for repeated use?
Answer: B
Explanation:
The correct answer is C . Forcepoint DSPM allows frequently used Enterprise Search filters to be saved as bookmarks, which is the appropriate method for Anna's weekly workflow. The documented Enterprise Search capability states that the page lets users access recently used filters, save frequently used filters as bookmarks, customize column headings, and generate meaningful reports. It also supports advanced filtering through GetVisibility Query Language , where a GQL query narrows scanned-file results to a focused, manageable subset for analysis and action.
Operationally, Anna would go to Enterprise Search , enter the complex GQL filter in the search/query field, execute or validate the filter, and then select the star icon to bookmark it. Forcepoint support documentation confirms that administrators can save complex GQL queries by using the star icon, creating a GQL bookmark, and providing an appropriate description.
The other options are incorrect because DSPM does not use an Excel import workflow to load reusable GQL filters from Analytics or Enterprise Search. Administration is also not the documented location for saving Enterprise Search GQL bookmarks. References/topics: Enterprise Search, GQL Filtering, Bookmarked Filters, Exporting Data, Reusable Search Workflows .
NEW QUESTION # 30
Provide the matching definitions for these Forcepoint DSPM Controls Orchestration terms:
Answer:
Explanation:
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 .
NEW QUESTION # 31
What are the key organizational elements for creating a dashboard in Forcepoint DSPM? Select two.
Answer: B,D
Explanation:
The correct answers are Widgets and Panels . In Forcepoint DSPM Analytics, dashboards are organized as boards that present scan-derived security, compliance, and data-risk information. The primary structural components used to build those dashboards are panels and widgets. Forcepoint states that dashboards are
"primarily constructed of panels and widgets," and explains that each panel can contain one or more widgets.
A panel acts as the container that organizes multiple visual components into a coherent dashboard layout, while a widget is the smaller information component that displays data from selected datasets such as files, trustees, connectors, or agents.
This structure matters because dashboards are not just static reports; they are configurable analytical workspaces. Widgets provide the actual data presentation, such as counters, charts, maps, text blocks, tables, incidents, dual data grouping, or multi-counter views. Forcepoint separately lists these as available widget types, each with its own customization options. Panels then provide the organizational layer that groups these widgets logically by use case, such as exposure, classification, incident review, user risk, or compliance posture. Toggle switches, menus, and status bars may appear in the interface, but they are not the dashboard- building elements. References/topics: Analytics, Components of a Dashboard, Widgets, Panels, Custom Dashboards .
NEW QUESTION # 32
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