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| Section | Objectives |
|---|---|
| Topic 1: Data Sources and Integration | - SIEM and external integrations - Database discovery and structured data scanning - Configuring data sources - Streaming and event integration |
| Topic 2: Deployment and Installation | - System requirements and prerequisites - Initial setup and configuration - Standalone DSPM deployment workflow |
| Topic 3: Data Classification and Detection | - Taxonomy and labeling mechanisms - Detectors and detector groups - Pattern matching and classification models |
| Topic 4: Administration and User Management | - User federation and Active Directory integration - Role-based access control (RBAC) - User synchronization and management |
| Topic 5: Monitoring, Analytics, and Reporting | - Scan progress and risk visibility - Dashboards and analytics widgets - Data lineage and compliance monitoring |
| Topic 6: Forcepoint DSPM Architecture and Overview | - Deployment models (cloud / standalone) - DSPM system components and platform overview |
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NEW QUESTION # 19
Which of the following utilizes artificial intelligence and machine learning when data is scanned?
Answer: D
Explanation:
The correct answer is D. Detectors . In Forcepoint DSPM, detectors are scan-time classification components that can contribute to the broader AI Mesh , now represented in the UI as the Classification Model .
Forcepoint describes the Classification Model as the mechanism that combines detection signals from keyword lists, regex patterns, machine-learning models, and topic classifiers into a pipeline that assigns classification tags such as Confidential , Internal , or Public . It also identifies Detector Groups as nodes that assess file paths and file contents using rule-based logic, including regular expressions and keywords.
This makes detectors the best answer among the listed options because they operate during scanning and feed classification outcomes. Forcepoint's detector documentation states that content detectors analyze file content to detect and categorize documents, and further notes that when a detector group is selected, the detector becomes part of the AI Mesh and contributes to classification results.
The other choices are not the primary AI/ML scan-time component. Patterns are regular-expression rules.
Data mapping defines Data Asset Inventory metadata fields. Compliance Hub manages governance policy artifacts, policy acceptance, reviews, and audit trail functions. References/topics: Detectors, AI Mesh, Classification Model, Content Detectors, Scan Classification Pipeline .
NEW QUESTION # 20
Geoff is configuring the Data Asset Inventory. Which Security Posture Policy Control options are available for him to use? Select three.
Answer: A,B,D
Explanation:
The correct options are Yes/No, Select, and Text . In Forcepoint DSPM, Security Posture Policy Controls are configured under Policy Center > Compliance Hub > Data Mapping . These controls define the metadata fields that appear in the Data Asset Inventory , allowing data owners to describe and govern critical data assets using consistent business and compliance attributes. Forcepoint describes Data Mapping as the framework used to define fields available in the Data Asset Inventory so data owners can assign metadata tags to critical data. Examples include retention period, business owner, regulatory scope, review status, and project name.
The documented control types are Text , Yes/No , and Select . Text creates a free-form text field, useful for values such as project name or business context. Yes/No creates a binary drop-down, appropriate for simple governance questions such as whether a review is required. Select creates a predefined selection menu aligned with common data governance frameworks.
GQL query is not a Data Mapping control type; GQL is used elsewhere for search and Controls Orchestration rule conditions. Checkbox is also not listed as a supported Security Posture Policy Control option. References/topics: Compliance Hub, Data Mapping, Security Posture Policy Controls, Data Asset Inventory, Metadata Governance .
NEW QUESTION # 21
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 # 22
You can configure your Forcepoint DSPM installation with Active Directory. What third-party product is used to import your AD users?
Answer: C
Explanation:
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 .
NEW QUESTION # 23
Which of the following is a key benefit of defining a custom data taxonomy?
Answer: C
Explanation:
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 .
NEW QUESTION # 24
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