CDPSE Privacy Engineering Practice Test 1 — 15 Questions

Question 1

A privacy engineer is implementing differential privacy on a dataset using Google Cloud's Differential Privacy library. Which THREE configuration steps are critical for minimizing re-identification risk?

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

Accepted Answer

Defining appropriate sensitivity bounds for the input data. Option B is correct because sensitivity bounds (clamping each record's contribution to a defined range, e.g., via bounded-sum or bounded-mean aggregations) directly limit how much any single individual can influence the output, which is a prerequisite for a valid differential privacy guarantee. Option D is correct because epsilon is the privacy-loss budget: a smaller epsilon means the output distributions for neighboring datasets are closer, so less information about any individual leaks, yielding stronger privacy (at the cost of utility). Option E is correct because the (epsilon, delta)-differential privacy formulation uses delta to bound the probability that the epsilon guarantee fails, providing the probabilistic relaxation needed for mechanisms such as the Gaussian mechanism and for composing many queries. Option A is wrong because a publicly known random seed does not weaken privacy—seeds are not secret keys in differential privacy, and the guarantee holds regardless of seed knowledge. Option C is wrong because dataset size cannot be made infinite and is not a configuration step; while larger datasets improve utility for a fixed epsilon, they are not required to minimize re-identification risk.

Answer

Choosing a random seed that is publicly known

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Increasing the dataset size to infinite

Question 2

In AWS Glue, a developer needs to ensure that sensitive columns are automatically identified and redacted during ETL jobs. Which component is best suited for this?

Accepted Answer

Glue DataBrew. AWS Glue DataBrew is a visual data preparation service with built-in PII detection and masking transforms, making it the natural fit for identifying and redacting sensitive columns during ETL. It provides pre-built recipes that can detect PII categories (names, SSNs, credit cards) and apply masking, substitution, or hashing transformations directly in the data preparation workflow. This aligns with the requirement to automatically identify and redact sensitive columns during ETL jobs.

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AWS Glue Schema Registry

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AWS Lake Formation

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Amazon Macie

Question 3

A privacy engineer is configuring a new AWS S3 bucket to store sensitive customer data. Which configuration ensures the highest level of privacy by design through encryption at rest using customer-managed keys?

Accepted Answer

Enable AWS KMS with Customer Managed Keys (SSE-KMS). SSE-KMS with Customer Managed Keys (CMKs) gives the organization full control over the key lifecycle — creation, rotation, disabling, and deletion — while AWS KMS performs the actual encryption and decryption. This satisfies privacy-by-design requirements for encryption at rest with customer-managed keys because the customer governs access via key policies and IAM, and can audit every key use through CloudTrail. It is the only option that combines AWS-managed encryption infrastructure with customer-controlled keys.

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Enable S3 Managed Keys (SSE-S3)

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Enable S3 Object Lock

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Enable Server-Side Encryption with Customer-Provided Keys (SSE-C)

Question 4

When implementing a 'Privacy Dashboard' for users, which feature is critical for fulfilling GDPR Article 15 (Right of Access) requests?

Accepted Answer

Automated data portability export. GDPR Article 15 grants data subjects the right to access their personal data and receive a copy of it in a commonly used, machine-readable format. An automated data portability export directly fulfills this by allowing users to download their data, which also supports Article 20 (Right to Data Portability). This feature ensures transparency and control, aligning with the Privacy Dashboard's purpose of empowering users.

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Account deletion button

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Consent management toggle

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Marketing preference settings

Question 5

In a database, you need to replace social security numbers with a consistent, non-reversible value for analytics. What is the best approach?

Accepted Answer

Salted one-way hashing. Salted one-way hashing produces a consistent, non-reversible value for each SSN, so the same SSN always yields the same hash (enabling joins and analytics) while the original cannot be recovered. A per-record or per-dataset salt prevents rainbow-table and frequency-analysis attacks, making it the best fit for analytics on SSNs where reversibility is not required.

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Deterministic encryption

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Format-preserving encryption

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Randomized tokenization

Question 6

When designing a data retention policy, which technical configuration in S3 best automates the process?

Accepted Answer

S3 Lifecycle policy. S3 Lifecycle policies are the native S3 mechanism for automating object transitions and expiration based on age or date, directly enforcing a data retention schedule without custom code. They can transition objects to cheaper storage classes (e.g., Standard-IA, Glacier) and ultimately expire (delete) them after a defined period, satisfying retention and disposal requirements. This makes them the correct technical control for automating retention policy enforcement in S3.

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S3 Replication

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S3 Inventory

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S3 Object Lock

Question 7

When designing a system with k-anonymity, which metric measures the impact of generalization on the utility of the data?

Accepted Answer

Discernibility penalty. The discernibility penalty is the metric used in k-anonymity to quantify the utility loss caused by generalization and suppression. It measures how distinguishable individual records become after anonymization, with higher values indicating greater information loss. This metric directly reflects the trade-off between privacy protection and data utility.

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Epsilon value

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Entropy loss

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F-score

Question 8

When encrypting data for long-term storage, which configuration is most important for privacy?

Accepted Answer

Frequent key rotation. Frequent key rotation limits the blast radius if a key is compromised, because ciphertext encrypted under retired keys becomes inaccessible once those keys are destroyed, and it satisfies regulatory expectations (NIST SP 800-57, PCI DSS Req. 3.6) for cryptoperiod management. For long-term storage, rotation combined with proper key destruction supports both confidentiality and crypto-shredding for erasure. It is the single most impactful configuration for sustained privacy protection.

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Disabling logging

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Hardcoding the key

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Using a single global key

Question 9

Which of the following is an effective way to implement 'Right to Erasure' in a distributed database system?

Accepted Answer

Asynchronous deletion across nodes. Asynchronous deletion across nodes allows the erasure request to propagate to all replicas without blocking the primary write path, ensuring eventual consistency of deletion while maintaining availability in a distributed system. This approach is standard for implementing GDPR Article 17 in sharded or replicated databases, where synchronous deletion across all nodes would create latency and availability problems. It must be paired with verification that all replicas and backups are purged within the regulatory timeframe.

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Deleting the entire database

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Marking records as deleted

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Overwriting with new user data

Question 10

You are auditing a system for 'Data Minimization'. Which discovery finding would be a primary concern?

Accepted Answer

Holding data beyond the retention period. Data minimization under GDPR Article 5(1)(c) requires that personal data be adequate, relevant, and limited to what is necessary for the stated purpose. Holding data beyond the retention period violates the storage limitation principle (Article 5(1)(e)) and is a direct indicator that minimization controls are failing. An auditor would flag this as a primary concern because it shows data is being kept without a lawful purpose.

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Using AES-256 for encryption

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Using a pseudonym for database keys

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Encrypting backups

Question 11

Which TWO techniques should be used to protect PII in non-production environments to ensure the data remains non-identifiable?

Accepted Answer

Pseudonymization. Pseudonymization (A) is correct because it replaces direct identifiers with artificial identifiers or tokens, so PII in non-production environments cannot be linked to a specific individual without separately held mapping data. Anonymization (E) is correct because it irreversibly removes or alters identifying details so the data can no longer be attributed to an identifiable person, which is the strongest way to keep non-production data non-identifiable. Full database mirroring (B) is not appropriate because it copies production PII intact into non-production, increasing exposure rather than protecting it. Public internet exposure (C) is clearly wrong because it makes PII more accessible to unauthorized parties. Plaintext replication (D) is also wrong because it moves readable PII without masking, tokenizing, or removing identifiers.

Answer

Full database mirroring

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Public internet exposure

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Plaintext replication

Question 12

You are configuring AWS Macie to identify PII in an S3 bucket. Which setting must be enabled to ensure that specific sensitive data patterns are detected across all files regardless of their object tags?

Accepted Answer

Custom data identifiers. Custom data identifiers in Amazon Macie let you define regex and keyword-based patterns (for example, a proprietary employee ID format or region-specific national ID) that Macie will scan for across objects, independent of object tags or classifications. Because the question requires detecting specific sensitive patterns regardless of tags, custom data identifiers are the correct mechanism. Managed identifiers cover common PII types but not organization-specific formats.

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IAM Role trust policy

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S3 Bucket Policy modification

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S3 Object Lock

Question 13

A privacy engineer is designing a data anonymization pipeline for a healthcare dataset. The dataset contains patient identifiers, demographics, and medical diagnoses. The goal is to release the data for research while preventing re-identification. Which technique should the engineer implement to protect against linkage attacks while preserving data utility for statistical analysis?

Accepted Answer

k-anonymity by generalizing and suppressing quasi-identifiers to ensure each record is indistinguishable from at least k-1 others.. k-anonymity is the appropriate technique because it specifically mitigates linkage attacks by ensuring that each record is indistinguishable from at least k-1 others based on quasi-identifiers. This preserves the statistical properties needed for research while reducing re-identification risk. Other methods either fail to address linkage or destroy data utility.

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Pseudonymization by replacing patient identifiers with randomly generated tokens.

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Homomorphic encryption to allow computations on encrypted data without decryption.

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Data masking by replacing all values with random characters, including medical diagnoses.

Question 14

A privacy engineer is configuring a web application to comply with the principle of data minimization. The application collects user data for a newsletter subscription. Which design change best implements data minimization?

Accepted Answer

Collect only the user's email address and store it in a dedicated database with strict access controls.. Data minimization requires collecting only personal data that is adequate, relevant, and limited to what is necessary for the specified purpose. For a newsletter subscription, the email address is the only necessary data. Collecting additional information or retaining it indefinitely fails to meet this principle.

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Collect the user's email address and IP address to detect fraudulent subscriptions.

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Collect the user's email address, full name, and phone number to personalize future communications.

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Collect the user's email address and retain it indefinitely to ensure future communications.

Question 15

A privacy engineer is evaluating a cloud-based customer analytics platform that processes personal data across multiple regions. The platform uses a shared responsibility model. Which two actions should the engineer take to ensure privacy compliance in the cloud environment? (Choose two.)

Accepted Answer

Verify that the cloud provider's data processing agreement (DPA) includes standard contractual clauses (SCCs) for cross-border data transfers.. The engineer should verify that the DPA includes SCCs for lawful cross-border transfers and implement CMEK to maintain control over data at rest. These actions directly address privacy compliance in a shared responsibility cloud model, ensuring legal transfer mechanisms and enhanced data protection.

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Store all personal data in a single region to avoid cross-border transfer issues.

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Conduct regular privacy impact assessments (PIAs) on the cloud provider's infrastructure.

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Rely on the cloud provider's default encryption settings for data in transit and at rest.