- A
Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.
Dataflow with DLP automates de-identification and creates a safe dataset.
- B
Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.
Why wrong: Shielded VM and VPC-SC provide security but do not de-identify data.
- C
Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.
Why wrong: Encryption does not remove identifiers; the team would still see PHI after decryption.
- D
Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.
Why wrong: Row-level security does not remove identifiers for training; it only masks at query time.
Quick Answer
The answer is to create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. This approach is correct because Cloud DLP provides native inspection and de-identification of PHI, such as patient names and SSNs, while Dataflow enables serverless, automated processing as part of a data pipeline, ensuring the de-identified data lands in a separate table for model training. On the Google Professional Machine Learning Engineer exam, this scenario tests your ability to integrate Cloud DLP with data processing services for compliance, often appearing as a trap where candidates mistakenly apply de-identification directly in Vertex AI Workbench or rely on BigQuery column-level security alone—neither of which removes the identifiers from the training data. The key insight is that de-identification must happen before the data reaches the data science team, not just restrict access. Memory tip: “DLP before DML”—run de-identification transforms before any data manipulation or model training.
PMLE Collaborating to manage data and models Practice Question
This PMLE practice question tests your understanding of collaborating to manage data and models. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.
Option A is correct because it uses Cloud DLP within a Dataflow pipeline to automatically de-identify PHI data as it is read from the original BigQuery table and written to a new, de-identified table. This satisfies the requirement for automated de-identification, while the original table remains encrypted at rest (BigQuery default) and in transit (TLS), and access to the original data can be logged via Cloud Audit Logs. The data science team only gets access to the de-identified table, ensuring PHI is not exposed during model development.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.
Why this is correct
Dataflow with DLP automates de-identification and creates a safe dataset.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.
Why it's wrong here
Shielded VM and VPC-SC provide security but do not de-identify data.
- ✗
Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.
Why it's wrong here
Encryption does not remove identifiers; the team would still see PHI after decryption.
- ✗
Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.
Why it's wrong here
Row-level security does not remove identifiers for training; it only masks at query time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between data masking/encryption (which still exposes PHI to authorized users) and true de-identification (which removes or transforms PHI so it is no longer considered protected health information).
Detailed technical explanation
How to think about this question
Cloud DLP provides over 150 built-in infoType detectors (e.g., US_SOCIAL_SECURITY_NUMBER, PERSON_NAME) that can be used in Dataflow templates to automatically transform sensitive fields. The de-identification transforms can include redaction, masking, tokenization, or pseudonymization, ensuring that the output BigQuery table contains no direct identifiers. Dataflow uses Apache Beam under the hood, allowing for scalable, exactly-once processing, and the pipeline can be triggered on a schedule or via Cloud Storage events for automation.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this PMLE question test?
Collaborating to manage data and models — This question tests Collaborating to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table. — Option A is correct because it uses Cloud DLP within a Dataflow pipeline to automatically de-identify PHI data as it is read from the original BigQuery table and written to a new, de-identified table. This satisfies the requirement for automated de-identification, while the original table remains encrypted at rest (BigQuery default) and in transit (TLS), and access to the original data can be logged via Cloud Audit Logs. The data science team only gets access to the de-identified table, ensuring PHI is not exposed during model development.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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