GCP Google Associate Data Practitioner Practice Test Exam

[2025] LATEST GCP ADP Google Cloud Platform Associate Data Practitioner Practice Exams Tests | Questions + Solutions

GCP Google Associate Data Practitioner Practice Test Exam
GCP Google Associate Data Practitioner Practice Test Exam

GCP Google Associate Data Practitioner Practice Test Exam free download

[2025] LATEST GCP ADP Google Cloud Platform Associate Data Practitioner Practice Exams Tests | Questions + Solutions

Are you ready to take on the GCP ADP Google Cloud Platform Associate Data Practitioner certification exam? Test your knowledge and boost your confidence with our high-quality premium practice questions, complete with detailed answer explanations. Each practice set is carefully designed to help you master core concepts and prepare effectively for the exam. At the end of each test, you’ll find DETAILED SOLUTION EXPLANATIONS to reinforce your understanding and enhance your problem-solving skills. These question sets are regularly updated to align with the latest syllabus changes and exam requirements, ensuring you stay ahead. Get ready to attempt your certification exam with confidence!



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SAMPLE QUESTION + SOLUTION EXPLANATION

Imagine you’re working on a project for Globetrekker, an online platform that helps users discover personalized travel itineraries. The platform gathers sensitive user information, such as preferences, travel history, and booking details, to customize its recommendations. You’re bringing in an external data engineer to help optimize a complex data transformation in a Google Cloud Dataflow pipeline. To ensure user privacy while the consultant contributes, what should you do?


A. Grant the consultant the Viewer role on the project.

B. Grant the consultant the Cloud Dataflow Developer role on the project.

C. Create a service account and allow the consultant to log on with it.

D. Create an anonymized sample of the data for the consultant to work with in a different project.



Explanation:

A is incorrect because granting the consultant the Viewer role on the project would allow them to view all resources and data within the project, including sensitive user information. This broad access poses a significant risk to user privacy, as the consultant could inadvertently or intentionally access personal data that is not necessary for their task. According to Google Cloud's IAM documentation, the Viewer role provides read-only access to all resources in a project, which is excessive for this scenario.


B is incorrect because assigning the consultant the Cloud Dataflow Developer role grants them permissions to manage and execute Dataflow jobs, which includes access to the data processed by these jobs. Given that the platform handles sensitive user information, this role would allow the consultant to access unmasked data, potentially compromising user privacy. It's essential to limit access to sensitive data to only those who absolutely need it.


C is incorrect because creating a service account and allowing the consultant to log in with it can lead to several security issues. Service accounts are intended for applications and services, not human users. Sharing service account credentials can result in a lack of accountability, as actions performed using the service account cannot be easily traced back to an individual. Additionally, this practice can violate the principle of least privilege, as the service account may have more permissions than necessary for the consultant's tasks.


D is correct because creating an anonymized sample of the data for the consultant to work within a different project ensures that sensitive user information is protected. By anonymizing the data, you remove personally identifiable information, reducing the risk of privacy breaches. Providing the consultant with access to a separate project containing only the anonymized data limits their permissions and exposure to sensitive information, adhering to the principle of least privilege. This approach aligns with data protection best practices, ensuring that individuals only have access to the data necessary for their work.



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