CDPSE Data Life Cycle Management — Timed 15 Questions

Question 1

When configuring AWS Macie to perform data minimization, which configuration is required to prevent the service from scanning internal metadata fields that do not contain customer PII?

Accepted Answer

S3 Exclusion Rules. AWS Macie uses S3 exclusion rules to filter out specific S3 buckets, objects, or prefixes from being scanned for sensitive data. By defining exclusion rules that target internal metadata fields (e.g., object keys or prefixes that contain system-generated metadata), Macie will skip those fields during its analysis, effectively preventing unnecessary scanning of non-PII data. This directly supports data minimization by limiting the scope of sensitive data discovery to only relevant customer data.

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S3 Bucket Policy

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Object Tagging

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VPC Endpoint Configuration

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IAM Policy Restriction

Question 2

In Collibra, you are mapping a data element to a business term to support data minimization. What is the effect of changing the 'Data Stewardship' workflow status to 'Deprecated'?

Accepted Answer

Archives the metadata record and removes it from search. In Collibra, setting the 'Data Stewardship' workflow status to 'Deprecated' indicates that the data element or business term is no longer in use. This action archives the metadata record, removing it from active search results while preserving it for historical reference. It does not delete physical data or prevent editing by unauthorized users.

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Triggers an immediate deletion task in the source DB

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Deletes the physical data source

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Prevents unauthorized users from editing the metadata

Question 3

While using the Salesforce Data Masking tool, which option should be selected to ensure that production data copied to a sandbox is rendered unusable for unauthorized parties?

Accepted Answer

Anonymization Masking. Anonymization Masking in Salesforce Data Masking replaces sensitive data with anonymized values so that sandbox users cannot identify individuals, rendering the data unusable for unauthorized parties. This is the specific masking type designed to irreversibly obscure production data copied to sandboxes. It satisfies privacy requirements for non-production environments.

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Object Permissions

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Field Level Security

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Data Refresh

Question 4

In Google Cloud Data Loss Prevention (DLP), which transformation method should be applied to a column containing email addresses to maintain format while ensuring privacy?

Accepted Answer

Format-Preserving Encryption. Format-Preserving Encryption (FPE) encrypts data while preserving its original format, so an email address remains a valid-looking email address after transformation. This satisfies the requirement to maintain format (e.g., for downstream systems that validate email syntax) while ensuring the underlying value is cryptographically protected and reversible only with the key. FPE is specifically designed for this use case in Google Cloud DLP.

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Redaction

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Masking

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Bucketization

Question 5

A CDPSE practitioner is configuring a data discovery job in OneTrust to identify PII in a structured database. Which setting must be enabled to ensure the tool automatically masks the data upon discovery based on a predefined classification tag?

Accepted Answer

Auto-Masking Rule. Auto-Masking Rule is the specific OneTrust configuration that applies a masking action (e.g., redaction, tokenization, or hashing) to data elements at the moment of discovery, driven by the classification tag assigned during the scan. Without an auto-masking rule bound to a classification, OneTrust will inventory and tag the PII but leave the underlying values intact. This directly satisfies the requirement to 'automatically mask upon discovery based on a predefined classification tag.'

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Data Retention Policy

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Workflow Trigger

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Classification Mapping

Question 6

You are utilizing Microsoft Purview to enforce a retention label on a SharePoint site. What occurs when the retention period expires?

Accepted Answer

The file is permanently deleted. In Microsoft Purview, when a retention label's retention period expires and the label is configured to 'retain items only' (or the disposition action is set to delete), the item is permanently deleted from the workload. For SharePoint, this means the file is removed from the site and from the preservation hold library, bypassing the standard recycle bin path for the retention-managed copy. This is the intended end-of-life action for a retention label whose purpose is deletion after the retention window.

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The file is moved to the recycle bin

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The file is encrypted

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The file becomes read-only

Question 7

Which tool within the SAP Data Intelligence suite is most appropriate for defining the lifecycle of data objects based on their business value?

Accepted Answer

Metadata Explorer. SAP Data Intelligence's Metadata Explorer is the catalog and governance component that lets you discover, profile, classify, and manage data assets, including defining lifecycle policies and retention rules tied to business value. It is the natural place to attach business-value-driven lifecycle definitions because it holds the metadata, lineage, and governance tags for data objects. The other tools handle pipelines, ML scenarios, and connectivity, not data-object lifecycle governance.

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Data Pipeline Designer

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Machine Learning Scenario Manager

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Connection Management

Question 8

When performing a data minimization assessment, which feature in Oracle Data Safe helps identify excessive data collection by flagging columns that contain sensitive data but are rarely queried?

Accepted Answer

Data Activity Auditing. Oracle Data Safe's Data Activity Auditing captures and analyzes actual SQL activity against the database, allowing you to see which columns are being accessed and how frequently. By correlating audit trails with Sensitive Data Discovery results, you can identify sensitive columns that are rarely or never queried, indicating potential over-collection. This directly supports data minimization by highlighting excessive data collection.

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Security Assessment

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Sensitive Data Discovery

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User Assessment

Question 9

A privacy analyst is conducting a data map in ServiceNow. Which feature allows them to link a database table directly to a 'Processing Activity' record?

Accepted Answer

Data Asset Mapping. Data Asset Mapping in ServiceNow allows the privacy analyst to link a database table directly to a Processing Activity record. This feature is part of the Privacy Management module and provides the technical and organizational linkage required for GDPR Article 30 records of processing activities. It creates a relationship between the data asset (e.g., a database table) and the processing activity, enabling traceability and impact assessments.

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Workflow Designer

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Configuration Item (CI) Relation

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Business Service Mapping

Question 10

You are configuring a data retention policy in AWS S3 Lifecycle rules. Which action should be selected to minimize data storage costs while remaining compliant with privacy laws?

Accepted Answer

Transition to Glacier. Transitioning objects to Glacier is the correct action to minimize storage costs while remaining compliant with privacy laws. Glacier offers significantly lower storage costs compared to S3 Standard or Standard-IA, and it retains data for long-term archival, which is often required by privacy regulations for audit or legal holds. This action moves objects to a cheaper storage class without deleting them, thus balancing cost and compliance.

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Enable Versioning

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Delete incomplete multipart uploads

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Expire objects

Question 11

A practitioner is setting up data minimization. Which feature in Google Cloud Spanner helps ensure that data is stored in specific regions for local compliance?

Accepted Answer

Placement Policies. Placement Policies in Google Cloud Spanner allow administrators to define where data is physically stored by specifying configurations tied to geographic regions (e.g., regional or multi-region instance configurations). This directly supports data residency and localization requirements needed for compliance regimes like GDPR or data sovereignty laws. By pinning data to specific regions, the practitioner enforces data minimization of jurisdictional exposure — data never leaves the approved geography.

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Encryption at Rest

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Access Control Lists

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Backup Retention

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Schema Validation

Question 12

Which TWO of the following are primary pillars of the Data Life Cycle Management?

Accepted Answer

Data Disposal. Data Disposal (A) is a primary pillar of Data Life Cycle Management because it defines the secure, compliant end-of-life handling of data—such as deletion, shredding, or cryptographic erasure—ensuring data no longer poses a risk or violates retention regulations. Data Collection (C) is also a primary pillar because it is the initial phase where data is acquired from sources and its quality, legality, and scope are established, forming the foundation for all later stages like storage, use, and sharing. Marketing Strategy (B) is a business function, not a data lifecycle phase, so it does not belong. Application Maintenance (D) concerns keeping software operational and is unrelated to governing data from creation to disposal. Network Security (E) is a protective control domain, not a distinct stage of the data lifecycle, so it is not one of the primary pillars.

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Marketing Strategy

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Application Maintenance

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Network Security

Question 13

Which THREE techniques effectively protect data during the 'use' phase of the life cycle?

Accepted Answer

Pseudonymization. Pseudonymization (C) is correct because it replaces direct identifiers with artificial pseudonyms during processing, so live data in use no longer exposes the subject's identity while still allowing re-linking via a separately held key. Data masking (D) is correct because it substitutes or obscures sensitive values (for example, showing only the last four digits of a card number) so that users and applications working with the data in the 'use' phase see only what they are authorized to see. Encryption (E) is correct because encrypting data at rest, in transit, and in use (for example, via TLS or application-level encryption) ensures that even if data is accessed during processing it remains unreadable without the correct keys. Data replication (A) is not a protection technique for the use phase; it copies data to additional locations, which increases exposure and attack surface rather than safeguarding it. Data compression (B) merely reduces storage and transmission size and provides no confidentiality, integrity, or access control benefit during use.

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Data replication

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Data compression

Question 14

A multinational bank is implementing a data minimization program for its customer analytics platform. The platform ingests transaction data, including customer names, account numbers, and transaction amounts. The privacy team wants to reduce privacy risk while preserving the ability to perform trend analysis. Which technique best achieves data minimization while maintaining analytical utility?

Accepted Answer

Apply differential privacy by adding calibrated noise to transaction amounts before analysis, and store only the noisy data.. Differential privacy is a formal technique that adds noise to data to protect individual privacy while enabling accurate aggregate analysis. It minimizes data by storing only the noisy version, reducing the risk of re-identification. This aligns with data minimization principles and preserves the ability to perform trend analysis, unlike tokenization or masking that retain original data.

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Aggregate transaction amounts by customer demographics and store only the aggregated results, discarding individual records.

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Mask customer names and account numbers in the analytics database, but retain the original values in a separate, access-controlled table for auditing.

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Tokenize customer names and account numbers using a format-preserving encryption scheme, and store the tokenization keys in a hardware security module (HSM).

Question 15

A multinational bank uses Microsoft SQL Server to store customer records across multiple regions. The privacy team must implement a data retention policy that automatically deletes records after seven years, but some records are subject to a legal hold that overrides deletion. Which SQL Server feature should be used to enforce this policy while ensuring legal holds are respected?

Accepted Answer

Data retention policies in Azure SQL Database with ledger and legal hold capabilities.. Azure SQL Database's data retention policies allow automated deletion after a set period, and ledger with legal hold capabilities ensure that records under hold are exempt. This integrated approach enforces the retention schedule while respecting legal requirements, unlike other features that lack built-in legal hold override or automatic enforcement.

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SQL Server Audit with a server audit specification that triggers deletion after seven years.

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Temporal tables with a retention policy configured on the history table.

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Stretch Database with a retention policy applied to the remote Azure SQL Database.