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| Section | Objectives |
|---|---|
| Topic 1: Data Discovery and Scanning | - Data Visibility and Monitoring - SMB Data Sources - Scan Configuration - LDAP Integration |
| Topic 2: Controls Orchestration | - Risk Classification - Webhook Configuration - Rule Creation and Management - Incident Investigation |
| Topic 3: Analytics and Dashboards | - Dashboard Customization - Enterprise Search - Risk Analysis and Reporting - Analytic Dashboards |
| Topic 4: Preparing and Deploying Forcepoint DSPM | - Data Source Configuration - DSPM Deployment Architecture - Lab Environment Preparation - User Management Configuration |
| Topic 5: Detector Configuration | - Regular Expression Detectors - Path Detectors - Detector Testing and Validation - Keyword Detectors |
| Topic 6: Pattern Matching and Classification | - Classification Policies - Regular Expressions - Sensitive Data Identification - Custom Pattern Creation |
| Topic 7: Compliance Hub and Data Register | - Data Asset Inventory - Metadata Management - Security Posture Policies - Departments Configuration |
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NEW QUESTION # 36
Which of the following statements describe real-world application of pattern matching in Forcepoint DSPM?
Select two.
Answer: C,D
Explanation:
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 .
NEW QUESTION # 37
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
NEW QUESTION # 38
Which of the following is an option to aid the process of reviewing the incidents generated from triggered rules in Controls Orchestration?
Answer: C
Explanation:
The correct answer is B . Forcepoint DSPM Controls Orchestration rules are built around query-driven rule logic. In the rule configuration workflow, administrators select a dataset such as Files , Trustees , or Agent Activities , then configure the rule condition using GQL syntax . This GQL condition defines which records match the rule and therefore which results appear as incidents when the rule is triggered. Forcepoint documentation states that Controls Orchestration rules identify data matching specific criteria and that the Condition field uses GQL syntax to filter the selected dataset.
When reviewing incidents, Forcepoint's Incidents page provides a top-down view of orchestration rules and available match results. Selecting an incident card opens a results preview, and choosing View in the Page displays those matched files in Enterprise Search , where additional review and action options are available.
Enterprise Search is specifically designed to narrow scanned-file results using GetVisibility Query Language
, and Forcepoint notes that GQL filtering helps reduce the result set to a more manageable subset for analysis and action.
Timestamp, Risk type, and keyword filtering may be useful in other contexts, but the documented advanced review mechanism for these results is GQL-based filtering. References/topics: Controls Orchestration, Incidents, Enterprise Search, GQL Filtering, Rule Match Review .
NEW QUESTION # 39
Which of the following utilizes artificial intelligence and machine learning when data is scanned?
Answer: C
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 # 40
What is the primary function of a detector in Forcepoint DSPM?
Answer: B
Explanation:
A detector in Forcepoint DSPM is a rule-based classification component used during scanning to identify files whose content or path matches defined detection logic. The correct answer is B because detectors are designed to locate specific patterns, words, or expressions and then contribute to classification/tagging outcomes. Forcepoint describes Detector Groups in AI Mesh as components where "detectors assess both the file path and its contents using rule-based logic, such as regular expressions and keywords," returning a match when the content fits the defined patterns.
This is distinct from AI classifiers, which infer sensitive-data meaning from broader semantic or machine- learning signals. Detectors are the more deterministic mechanism: they look for defined indicators such as a keyword, phrase, regular expression, or path element. Forcepoint release notes also describe detector functionality as allowing content or path search when creating a detector, including an example where adding the word Archive to a detector can tag files located in an Archive folder.
The other options confuse detectors with unrelated platform functions. Detectors do not create credential tags specifically, optimize network throughput, block files for speed, or increase storage capacity. References
/topics: Detectors, Pattern Matching, AI Mesh, Classification Tags, Content and Path Search .
NEW QUESTION # 41
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