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| Section | Objectives |
|---|---|
| Topic 1: Preparing and Deploying Forcepoint DSPM | - User Management Configuration - DSPM Deployment Architecture - Lab Environment Preparation - Data Source Configuration |
| Topic 2: Detector Configuration | - Keyword Detectors - Path Detectors - Regular Expression Detectors - Detector Testing and Validation |
| Topic 3: Pattern Matching and Classification | - Classification Policies - Regular Expressions - Sensitive Data Identification - Custom Pattern Creation |
| Topic 4: Compliance Hub and Data Register | - Departments Configuration - Data Asset Inventory - Metadata Management - Security Posture Policies |
| Topic 5: Controls Orchestration | - Incident Investigation - Rule Creation and Management - Webhook Configuration - Risk Classification |
| Topic 6: Analytics and Dashboards | - Enterprise Search - Risk Analysis and Reporting - Analytic Dashboards - Dashboard Customization |
| Topic 7: Data Discovery and Scanning | - Scan Configuration - LDAP Integration - Data Visibility and Monitoring - SMB Data Sources |
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NEW QUESTION # 42
Geoff is configuring the Data Asset Inventory. Which Security Posture Policy Control options are available for him to use? Select three.
Answer: B,C,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 # 43
What is the purpose of assigning compliance tags to data patterns in Forcepoint DSPM?
Answer: A
Explanation:
Compliance tags in Forcepoint DSPM are used to associate detected data patterns with regulatory or compliance obligations. The correct answer is B because these tags help the platform identify files and data objects that may fall under privacy, security, or industry-specific regulatory regimes. Forcepoint's pattern- matching terminology defines Compliance as tags that help organizations conform to regulatory regimes, and gives the example of applying tags such as GDPR/PII to a Social Security number pattern so related documents can be identified.
Operationally, this means a pattern is not only detecting a technical string, such as a national identifier, payment card number, or health-related value; it is also attaching compliance meaning to the match. That compliance context can then support classification, filtering, analytics, remediation prioritisation, and audit evidence. Forcepoint also states that when adding a new pattern, selected Classification, Compliance, and Distribution values can override machine-learning model output during scans, making the assigned tags authoritative for scan results.
The other options are adjacent but incorrect. User monitoring and reporting are downstream analytics activities, encryption policy management is not the core purpose of pattern compliance tagging, and user credentials are handled through identity and access management workflows. References/topics: Pattern Matching, Compliance Tags, Classification Model, AI Mesh, Regulatory Data Identification .
NEW QUESTION # 44
Which of the following are steps in creating a data classification and handling policy? Select four.
Answer: A,C,D,E
Explanation:
The correct selections are A, C, D, and E . A data classification and handling policy is a governance artifact that defines how data assets are categorized, owned, protected, reviewed, and aligned to legal or regulatory requirements. In Forcepoint DSPM, this sits within Policy Center and Compliance Hub , which are designed to support data classification, policy creation, security-control implementation, compliance, and risk reduction. Forcepoint states that Policy Center streamlines policy creation and prompts organizational representatives to provide information needed to build a governance framework aligned to organizational goals.
Specifying asset requirements maps to Data Mapping, where organizations define Data Asset Inventory fields and metadata such as retention period, business owner, regulatory scope, review status, and project name. Incorporating industry-specific regulations is required because departmental policy design must reflect obligations such as GDPR, HIPAA, and PCI DSS. Defining classification levels aligns to Taxonomy, where sensitivity levels such as Confidential, General Business, Public, and Highly Confidential are configured. Outlining roles and responsibilities aligns to assigning policy owners, department heads, representatives, and accountable stakeholders. Network traffic protocols are not a core step in creating a data classification and handling policy; they belong to network architecture or transport-control design, not data governance policy creation.
NEW QUESTION # 45
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 # 46
Which of the following is NOT a widget type available in Forcepoint DSPM dashboards?
Answer: A
Explanation:
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 .
NEW QUESTION # 47
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