Real4dumps DSPM-Deploy-and-Administer Dumps Real Exam Questions Test Engine Dumps Training [Q13-Q29]

The correct selections are Identifying high-risk users , Identifying ROT data , and Identifying critical data assets . Forcepoint DSPM Analytics includes pre-defined dashboards, or boards, that summarize scan findings into operational views for security, compliance, and data-governance teams. Forcepoint states that Analytics boards present scan-derived metrics such as overexposed files, sensitive data, and data at risk, helping teams make informed decisions about vulnerabilities, risks, and compliance issues.
The pre-defined board list directly supports the three correct options. High Risk Users identifies users with excessive permissions or abnormal access patterns, supporting insider-threat reduction and least-privilege enforcement. ROT Data identifies redundant, obsolete, or trivial data so teams can reduce clutter, unnecessary exposure, and policy risk. Key Data Overview highlights critical data assets, allowing teams to prioritize protection of the organization’s most valuable and sensitive information.
The other answers are not the best matches for dashboard simplification in this question. Database assets are handled through structured-data discovery and database-specific workflows, not the named dashboard use cases here. User credentials may be detected as sensitive content in scans, but identifying credentials is not listed as a primary pre-defined dashboard task. References/topics: Analytics, Pre-defined Boards, Data Classification, Data Hygiene, Data Risk, High Risk Users, ROT Data, Key Data Overview .

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 .

In Forcepoint DSPM, data ownership is treated as an explicit governance assignment tied to the organisation’s data asset inventory and data sources, not as a passive attribute inferred only from file metadata or imported directory fields. The closest correct option is C , because ownership is assigned at the data storage/data source or data asset level so that accountability, alert routing, review responsibility, and remediation workflows have a clearly identified business owner.
Forcepoint documentation describes the Data Asset Inventory workflow as requiring organisations to “assign an owner” for each asset and states that DSPM can “assign specific data owners to each data source.” This aligns with DSPM’s governance model: the owner is the accountable stakeholder for the data asset, not merely the technical file creator or last modifier. Release notes also describe a Data Owner column on the Data Assets page, where ownership may be automatically selected based on department owner and manually changed by the user.
Therefore, A is incorrect because policies define controls and requirements, not the owner itself. B is too narrow because imported user properties support identity context but do not alone define data ownership. D is incorrect because file owner metadata is not the authoritative ownership model for DSPM governance.
References/topics: Data Register, Data Asset Inventory, Data Ownership, Controls Orchestration, Access Governance .

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
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The correct selections are Regular expressions and Classification tags . In Forcepoint DSPM pattern matching, a pattern is built around a RegEx , meaning the text sequence or structure the system searches for during file scans. Forcepoint defines RegEx as “a sequence or pattern that is searched for in text,” and defines a pattern as the RegEx plus the rules associated with its detection. That makes regular expressions the core detection mechanism for custom pattern matching.
Classification tags are also a key configuration component because they define how matching files should be labelled after the pattern is detected. In the Add New Pattern workflow, Forcepoint lists Classifications as a configurable field and describes it as the “Classification tagset value.” The same workflow states that selected Classification, Compliance, and Distribution values override machine-learning model output during endpoint suggestions and file scans.
The remaining options are not primary pattern-matching configuration components. Applicable Country is not listed as a core pattern field in the documented pattern workflow. User credentials belong to identity and access management, not detection logic. Data sensitivity keys is not the documented configuration term used for pattern creation. References/topics: Pattern Matching, RegEx Detection, Classification Tags, Compliance Tags, Distribution Tags, Scan Classification Overrides .

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 .

The correct answer is B. Heatmap . Forcepoint DSPM dashboards are built from configurable widgets that present scan, access, classification, and risk data in different visual formats. The official Forcepoint DSPM widget list includes Counter , Chart , Map , Text , Table , Incidents , Dual Data Grouping , and Multi Counter . Heatmap is not listed as an available dashboard widget type.
The other answer choices are valid widget types. Counter widgets provide quick numerical summaries, such as counts of sensitive files, risky users, or matching records from selected datasets. Forcepoint describes Counter widgets as useful for at-a-glance metrics and supports aggregation functions such as count, sum, average, min, max, and median. Chart widgets visualize grouped data using supported chart formats, including horizontal bar, vertical bar, line, area, or pie charts. Text widgets are also part of the documented widget list and are used for static explanatory or contextual information on a board.
Therefore, Heatmap is the non-valid option. References/topics: Analytics, Dashboards, Widgets, Counter, Chart, Text, Dashboard Components .

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 .

Explanation:
1 # 3 # 4 # 2
The correct sequence is Select the Edit Widgets button , Open the Widget settings , Edit the GQL filter , and then Save the updated filter . In Forcepoint DSPM Analytics, a duplicated dashboard must first be placed into a widget-editable state before any individual widget configuration can be changed. Forcepoint describes dashboards as being built from panels and widgets , and identifies Edit Widgets as the control used to open the edit view for modifying widgets on a board. ( help.forcepoint.com ) After edit mode is active, the administrator opens the specific widget’s settings because the filter is scoped to that widget rather than the whole dashboard. The GQL filter is then edited to constrain the widget’s selected dataset, such as files, trustees, connectors, agents, database tables, or other analytics objects. This follows Forcepoint’s dashboard model, where widgets present information from selected datasets and have their own customization options. ( help.forcepoint.com ) The final action is saving the updated filter so the duplicated dashboard preserves the modified widget logic. References/topics: Analytics, Dashboard Components, Edit Widgets, Widget Settings, GQL Filters, Custom/Duplicated Dashboards .

The correct answer is D. To monitor the progress of ongoing data scans . The Scan Status dashboard is used to track scan execution and understand where a scan is in the discovery and classification lifecycle.
Forcepoint’s scan analytics documentation describes scan status views that show the current discovery-stage status, last status update, discovered-file count, and detailed scan analytics for a selected data-source configuration. It also explains that the Scan Progress tab shows the selected scan configuration, discovery- stage status, last discovered file time, scan start time, scan number, classification status, and classification timing.
This makes the dashboard operationally useful for administrators who need to verify that scans are running, determine whether discovery or classification is still in progress, and identify whether scan activity has stalled or produced errors. It is not intended to display encrypted files, track department membership, or monitor network traffic. Department-level visibility is handled through Compliance Hub and Data Asset Inventory workflows, while network activity monitoring is outside the scope of the Scan Status dashboard. References
/topics: Dashboard, Scan Status, Scan Analytics, Scan Progress, Discovery Status, Classification Status, Data Source Scanning .

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 .

The correct answer is D. Head of Department . In Forcepoint DSPM Compliance Hub, Departments represent organisational business units such as HR, Finance, or Payment Processing. They are used to mirror the company’s functional structure inside the governance workflow so that policies, data assets, ownership responsibilities, and review activities can be aligned to the correct business stakeholders. Forcepoint states that Departments are configured by defining company departments and assigning department representatives, with department heads or business-unit representatives acting as primary points of contact.
The documented workflow for adding a department is explicit: click Add new department , then enter the Department name , Head of Department , and Notify At email address, then save. This makes Head of Department the required field among the options listed. The field is important because department heads receive governance-related notifications and, when they log in, see only the Security Posture Policies and Data Asset Inventory assigned to their department.
The distractors belong to other workflows. Chief Reviewer is not the department-creation field. Group Name relates to user or group administration, not Compliance Hub department definition. GQL data mapping is used for identifying data assets within DSPM policy/data-mapping logic, not for creating a department.
References/topics: Compliance Hub, Departments, Department Representatives, Policy Center, Data Asset Inventory visibility .

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 .

Explanation:
Click EDIT WIDGETS in the upper-right area of the dashboard.
Ella should click the EDIT WIDGETS button near the upper-right side of the dashboard, beside the EXPORT PDF button. This control opens the dashboard edit view, which exposes the controls required to modify dashboard content, including adding widgets and adding panels. In the screenshot, the correct click target is the green-outlined EDIT WIDGETS button above the dashboard panels.
Forcepoint documents dashboards as being primarily constructed from panels and widgets . A panel is the container that organizes one or more widgets, while widgets display selected datasets such as files, trustees, connectors, agents, or other DSPM analytics data. The Forcepoint documentation specifically states: “Edit Widgets: Click the Edit Widget button, to open the edit view.” It then lists Add widget and Add panel as functions available from that edit mode.
Therefore, Ella cannot add a panel by clicking the existing charts, the left navigation list, or the dashboard title. She must first enter edit mode through EDIT WIDGETS , then use the available Add panel option.
References/topics: Analytics, Components of a Dashboard, Edit Widgets, Add Panel, Panels and Widgets
.

Explanation:
Administration > Taxonomy
Taxonomy tags are managed from the Administration menu, specifically under Administration > Taxonomy . In the screenshot, Adam should not select Policy Center ; the currently open menu shows Compliance Hub, Data Register, Controls Orchestration, and Incidents, but taxonomy management is not located there. The correct UI action is to open the Administration drop-down on the top navigation bar and select Taxonomy .
Forcepoint’s DSPM documentation states that the Taxonomy page displays predefined AI Mesh tags, which cannot be removed, and also allows administrators to add custom tags for pattern matching and detection rules. It further explains that taxonomy mapping can connect AI Mesh tags with data-source taxonomies so mapped tags can be written back as the organization’s own labels.
This location is important because taxonomy governs the classification language DSPM applies to scanned data. By updating taxonomy tags, Adam aligns DSPM classification output with the company’s internal handling policies, sensitivity labels, and compliance terminology. Forcepoint also notes that new taxonomy tags are created from the Taxonomy tab under the Administration section, and that applied tags are visible in the Data Asset Inventory. References/topics: Administration, Taxonomy, AI Mesh Tags, Classification Tags, Pattern Matching, Detection Rules .

The correct answer is Keycloak . In Forcepoint DSPM standalone deployments, user authentication and user import workflows are handled through the embedded Keycloak administration console. The official Forcepoint DSPM documentation states that “AD Users are added to Forcepoint DSPM through Keycloak” and then describes the workflow for configuring Keycloak’s User Federation capability. The process begins by opening the Keycloak admin console, logging in as an administrator, confirming that the gv realm is selected, navigating to User Federation , and adding an LDAP provider for the Active Directory source.
The distractors map to adjacent but incorrect concepts. User Federation is not the third-party product; it is the Keycloak feature used to connect LDAP or Active Directory identity stores. Kubernetes provides container orchestration and is not responsible for directory import. Rancher is used in platform or cluster management contexts, not AD identity synchronization. Okta can function as an identity provider in many enterprise environments, but it is not the specific product identified in the DSPM AD import workflow.
References/topics: User Management, Keycloak Administration, gv Realm, Active Directory Integration, LDAP Provider, User Federation .