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Answer: 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type. 2. Load the booking and user profile data into a BigQuery table.

The correct answer is option D. Here's why: Option D is correct because it suggests using format-preserving encryption with FFX as the de-identification transformation type. This method encrypts sensitive data (like email addresses) while maintaining the original data's format and length, allowing analysts to perform analysis without accessing actual email addresses. Other options either obscure the data too much for analysis (A and B) or focus on masking rather than de-identifying (C), which doesn't meet the analysts' needs for data analysis while protecting PII.

Author: LeetQuiz Editorial Team

Quick Answer

Answer-first summary for fast verification

Author: LeetQuiz Editorial Team

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Your company's data platform receives CSV files containing booking and user profile data, stored in Cloud Storage. The goal is to combine these datasets using the email field for analysis, while ensuring personally identifiable information (PII) remains protected. How should you de-identify the email field before loading the data into BigQuery for analysis?

Real Exam

Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the email mask as the data masking rule. 3. Assign the policy to the email field in both tables. 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

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Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type. 2. Load the booking and user profile data into a BigQuery table.

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Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the default masking value as the data masking rule. 3. Assign the policy to the email field in both tables. 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

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Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type. 2. Load the booking and user profile data into a BigQuery table.