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| Section | Objectives |
|---|---|
| Pattern Matching and Classification | - Regular Expressions - Custom Pattern Creation - Sensitive Data Identification - Classification Policies |
| Detector Configuration | - Regular Expression Detectors - Keyword Detectors - Detector Testing and Validation - Path Detectors |
| Compliance Hub and Data Register | - Metadata Management - Departments Configuration - Data Asset Inventory - Security Posture Policies |
| Analytics and Dashboards | - Dashboard Customization - Enterprise Search - Risk Analysis and Reporting - Analytic Dashboards |
| Data Discovery and Scanning | - Scan Configuration - Data Visibility and Monitoring - SMB Data Sources - LDAP Integration |
| Preparing and Deploying Forcepoint DSPM | - DSPM Deployment Architecture - Lab Environment Preparation - User Management Configuration - Data Source Configuration |
| Controls Orchestration | - Rule Creation and Management - Webhook Configuration - Incident Investigation - Risk Classification |
>> Reliable DSPM-Deploy-and-Administer Test Questions <<
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NEW QUESTION # 12
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: D
NEW QUESTION # 13
Which of the following are key configuration components of pattern matching in Forcepoint DSPM? Select two.
Answer: B,E
Explanation:
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 .
NEW QUESTION # 14
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 # 15
When creating a Detector, which field allows you to exclude specific criteria from the search?
Answer: C
Explanation:
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 .
NEW QUESTION # 16
How are data owners defined in Forcepoint DSPM?
Answer: C
Explanation:
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 .
NEW QUESTION # 17
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