CDPSE Privacy Engineering Practice Test 2 — 15 Questions

Question 1

A privacy engineer is configuring a database that stores customer personal data. The engineer must ensure that data is protected in the event of a physical disk theft. Which control is most appropriate?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable transparent data encryption (TDE) on the database.. Transparent data encryption (TDE) is the most appropriate control because it encrypts the entire database at rest, including data files and backups, protecting against physical disk theft. Other options like column-level encryption, auditing, or self-encrypting drives either do not fully protect the database files or are less integrated with database operations.

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Configure database auditing to log all access to personal data.

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Store the database on a self-encrypting drive (SED) and manage keys in a hardware security module (HSM).

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Implement column-level encryption for all columns containing personal data.

Question 2

A privacy engineer is designing a system to support the GDPR right to data portability. The system must allow users to export their data in a structured, commonly used, and machine-readable format. Which two technical requirements must be met to ensure the export is both interoperable and secure? (Choose two.)

Accepted Answer

The export must be encrypted in transit and at rest, and accessible only to the authenticated user.. Data portability requires that the export include all personal data provided by the user and data generated by their activity, ensuring completeness. Additionally, the export must be secured via encryption and authentication to prevent unauthorized access. Proprietary formats, public URLs, and PDFs fail to meet interoperability or security requirements. Thus, the two correct requirements are content completeness and secure delivery.

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The export format must use a proprietary binary encoding to minimize size.

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The export format must be a human-readable PDF to ensure users can review it.

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The export must be delivered via an unauthenticated public URL for convenience.

Question 3

A privacy engineer is configuring Azure Data Factory to ensure PII is masked during integration. Which feature should be configured to apply dynamic data masking on SQL targets?

Accepted Answer

SQL Dynamic Data Masking policies. Dynamic data masking (DDM) on SQL targets is a database-engine feature configured via SQL DDM policies, which mask column values at query time for non-privileged users without altering stored data. In Azure, this is enabled on Azure SQL Database, Azure SQL Managed Instance, and SQL Server via T-SQL (CREATE MASKED COLUMN) or the Azure portal's Dynamic Data Masking blade. Azure Data Factory itself does not apply DDM; it relies on the target SQL engine's masking policy.

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Azure Policy definition

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Data Factory Mapping Data Flows

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Azure Key Vault access policies

Question 4

A practitioner is deploying a Google Cloud Storage bucket. To ensure that files containing PII are automatically redacted before being accessed by external users, which Cloud DLP action should be integrated?

Accepted Answer

Cloud DLP de-identification template. Cloud DLP de-identification templates define the specific transformation rules (e.g., masking, tokenization, redaction) applied to sensitive data like PII. Integrating a de-identification template with Cloud Storage allows automatic redaction of PII before external users access the files, satisfying the requirement for automated, policy-driven data minimization. This is the native Google Cloud mechanism for content-level redaction, not access control or lifecycle management.

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IAM conditions

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Storage Transfer Service

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Bucket lock

Question 5

In a Google Cloud environment, you need to implement a policy to automatically redact PII from documents uploaded to Cloud Storage. Which service should be integrated?

Accepted Answer

Cloud Data Loss Prevention (DLP) API. Cloud Data Loss Prevention (DLP) API is the Google Cloud service designed to discover, classify, and redact sensitive data like PII. Integrating the DLP API with Cloud Storage allows automatic scanning and redaction of uploaded documents based on configured infoType detectors and de-identification templates. This is the native, scalable solution for content-level redaction.

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Cloud Storage buckets lifecycle policy

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Cloud Identity and Access Management

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Cloud Logging

Question 6

When implementing Privacy by Design (PbD) in a new mobile application, which TWO of the following are considered proactive technical controls?

Accepted Answer

Default privacy settings. Option D (Default privacy settings) is a proactive technical control because Privacy by Design's "Privacy by Default" principle requires that the application's configuration automatically applies the most privacy-protective settings without requiring user action, ensuring data is protected from the first moment of use. Option E (Data minimization) is likewise proactive and technical: it is implemented in the application's data model, APIs, and collection logic so that only the minimum personal data necessary for the stated purpose is gathered, processed, and retained, reducing exposure before any incident occurs. By contrast, option A (Periodic external audits) is a reactive/detective governance activity performed after implementation rather than an embedded technical control. Option B (End-user training) is an administrative/organizational awareness measure, not a technical control, and option C (Comprehensive incident response plan) is a reactive procedural document that addresses events after they happen rather than preventing them by design.

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Periodic external audits

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End-user training

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Comprehensive incident response plan

Question 7

A privacy engineer is configuring Azure SQL Database to ensure that sensitive columns containing PII are hidden from non-privileged users. Which feature should be implemented to achieve dynamic data masking?

Accepted Answer

Dynamic Data Masking. Dynamic Data Masking (DDM) in Azure SQL Database masks sensitive columns in query results for non-privileged users without altering the stored data. It is configured per-column with masking functions (default, email, random, partial) and applies at query time based on user permissions. This directly satisfies the requirement to hide PII from non-privileged users.

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Always Encrypted

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Row-Level Security

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Transparent Data Encryption

Question 8

A privacy engineer is using Terraform to enforce encryption at rest for S3 buckets. Which resource attribute should be set to 'aws:kms'?

Accepted Answer

server_side_encryption_configuration. In Terraform, the `server_side_encryption_configuration` block within an `aws_s3_bucket` resource is used to configure default encryption for the bucket. Setting its `sse_algorithm` to `aws:kms` enables SSE-KMS encryption at rest. This is the correct attribute to enforce encryption.

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bucket_acl

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versioning_enabled

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public_access_block

Question 9

Which technique is most effective for minimizing PII in a non-production database environment while maintaining referential integrity?

Accepted Answer

Tokenization. Tokenization replaces PII with non-sensitive surrogate tokens while preserving the format and referential relationships via a secure token vault, so joins and foreign keys still work in non-production environments. It is the most effective technique for minimizing PII exposure while maintaining referential integrity because the same input value consistently maps to the same token, and the original data is not recoverable without vault access.

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Full deletion

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Masking with random noise

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Hashing with a static salt

Question 10

In the context of Privacy by Design, what is the primary role of an 'Access Control Matrix' in a microservices architecture?

Accepted Answer

Principle of least privilege implementation. In a microservices architecture, an Access Control Matrix defines which service (or role) can invoke which API or access which resource, directly implementing the principle of least privilege by granting only the minimum permissions needed. It is a Privacy by Design control because it limits data exposure and enforces authorization at service boundaries, reducing the blast radius of a compromised service.

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Log aggregation

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Threat modeling

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Data classification

Question 11

Which of the following is an example of a Privacy-Enhancing Technology (PET) that focuses on data minimization?

Accepted Answer

Synthetic data generation. Synthetic data generation is a Privacy-Enhancing Technology that focuses on data minimization because it creates artificial records that preserve statistical properties of the original dataset without containing any real personal data. This allows organizations to use, share, or analyze data without processing actual personal information, directly supporting the minimization principle. HSMs, AES-256, and vulnerability scanning are security controls, not data minimization PETs.

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Hardware Security Modules (HSMs)

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AES-256 Encryption

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Vulnerability scanning

Question 12

When implementing differential privacy in a data analytics pipeline, what is the primary technical trade-off the engineer must balance?

Accepted Answer

Privacy budget and accuracy. Differential privacy works by adding calibrated noise (e.g., Laplace or Gaussian) to query results, and the amount of noise is governed by the privacy budget (epsilon, ε). A smaller epsilon means stronger privacy but more noise, which reduces the accuracy and utility of analytical results. The engineer must therefore balance the privacy budget against analytical accuracy — spending too much budget weakens privacy guarantees, while spending too little renders the data useless.

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Anonymization speed and data volume

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Encryption speed and latency

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Scalability and storage costs

Question 13

When using Homomorphic Encryption, what is the primary limitation for a privacy engineer to consider?

Accepted Answer

Computational overhead. Homomorphic encryption allows computations on encrypted data without decryption, but the mathematical operations are extremely resource-intensive, leading to significant computational overhead. This overhead is the primary limitation because it can make real-time or large-scale processing impractical. Other options like data loss or inability to search are not inherent limitations of homomorphic encryption.

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Data loss during processing

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Inability to perform search

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Lack of vendor support

Question 14

You are designing a system for k-anonymity. If a dataset has an identifier that is unique to every row, what is the first step you must take before applying generalization?

Accepted Answer

Apply suppression to direct identifiers. Before generalization can produce meaningful equivalence classes, direct identifiers (names, SSNs, email addresses) that uniquely identify each row must be removed or suppressed. If a unique direct identifier remains, every row stays distinguishable regardless of how much you generalize quasi-identifiers, defeating k-anonymity. Suppression of direct identifiers is therefore the mandatory first step in the standard k-anonymity pipeline (remove direct identifiers, then generalize/suppress quasi-identifiers).

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Add synthetic noise

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Normalize the dataset

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Increase the k-value

Question 15

A company needs to share customer demographics with a third party. Which technique allows for statistical analysis without revealing individual identities?

Accepted Answer

Differential privacy. Differential privacy adds calibrated statistical noise to query results so that aggregate analysis is possible while individual records cannot be distinguished or re-identified. It provides a mathematical privacy guarantee, making it suitable for sharing demographics for statistical analysis without revealing identities.

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Encryption

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Data deletion

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Pseudonymization