EXCELLENT PROFESSIONAL-CLOUD-SECURITY-ENGINEER VALID TEST TESTKING COVERS THE ENTIRE SYLLABUS OF PROFESSIONAL-CLOUD-SECURITY-ENGINEER

Excellent Professional-Cloud-Security-Engineer Valid Test Testking Covers the Entire Syllabus of Professional-Cloud-Security-Engineer

Excellent Professional-Cloud-Security-Engineer Valid Test Testking Covers the Entire Syllabus of Professional-Cloud-Security-Engineer

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The Google Professional-Cloud-Security-Engineer Exam covers a wide range of topics, including security management, data protection, network security, and compliance. Professionals who pass Professional-Cloud-Security-Engineer exam will have demonstrated their ability to implement security controls to protect data, secure network infrastructure, and manage security operations in Google Cloud environments.

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q302-Q307):

NEW QUESTION # 302
When creating a secure container image, which two items should you incorporate into the build if possible?
(Choose two.)

  • A. Ensure that the app does not run as PID 1.
  • B. Remove any unnecessary tools not needed by the app.
  • C. Package a single app as a container.
  • D. Use many container image layers to hide sensitive information.
  • E. Use public container images as a base image for the app.

Answer: B,C

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/best-practices-for-building-containers


NEW QUESTION # 303
Your organization must comply with the regulation to keep instance logging data within Europe. Your workloads will be hosted in the Netherlands in region europe-west4 in a new project. You must configure Cloud Logging to keep your data in the country.
What should you do?

  • A. Configure the organization policy constraint gcp.resourceLocations to europe-west4.
  • B. Set the logging storage region to eurcpe-west4 by using the gcloud CLI logging settings update.
  • C. Create a new tog bucket in europe-west4. and redirect the _Def auit bucKet to the new bucket.
  • D. Configure log sink to export all logs into a Cloud Storage bucket in europe-west4.

Answer: D

Explanation:
To comply with the regulation to keep instance logging data within Europe, specifically in the Netherlands (region europe-west4), you need to configure Cloud Logging to ensure that all logs are stored in a specified region. Here are the steps to achieve this:
* Create a Cloud Storage Bucket:
* Go to the Google Cloud Console.
* Navigate to Cloud Storage and create a new bucket.
* Set the location type to "Region" and select "europe-west4" as the region.
* Configure Log Sink:
* Go to the Logging section in the Google Cloud Console.
* Create a new log sink and configure it to export logs to the Cloud Storage bucket you just created.
* Ensure the sink includes all logs you want to keep within europe-west4.
* Verify Configuration:
* Ensure that the logs are being exported to the Cloud Storage bucket by checking the contents of the bucket.
* This setup ensures that all your logging data remains within the specified region, adhering to compliance requirements.
References
* Creating and Managing Log Sinks
* Cloud Storage Locations


NEW QUESTION # 304
Your organization is developing a sophisticated machine learning (ML) model to predict customer behavior for targeted marketing campaigns. The BigQuery dataset used for training includes sensitive personal information. You must design the security controls around the AI/ML pipeline.
Data privacy must be maintained throughout the model's lifecycle and you must ensure that personal data is not used in the training process. Additionally, you must restrict access to the dataset to an authorized subset of people only. What should you do?

  • A. De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP)APIs.
    and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
  • B. Implement Identity-Aware Proxy to enforce context-aware access to BigQuery and models based on user identity and device.
  • C. Deploy the model on Confidential VMs for enhanced protection of data and code while in use.Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
  • D. Implement at-rest encryption by using customer-managed encryption keys (CMEK) for the pipeline. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

Answer: A

Explanation:
Data De-identification: De-identifying sensitive data using Cloud DLP APIs ensures that the data used for model training does not contain personally identifiable information (PII). This protects data privacy and reduces the risk of unauthorized access or misuse.
IAM Policies: Implementing strict IAM policies controls access to BigQuery, ensuring that only authorized personnel can access and use the dataset. This further protects data privacy and reduces the risk of unauthorized access.
Comprehensive Approach: This approach combines data de-identification and IAM controls to provide a robust and effective security solution for the AI/ML pipeline.


NEW QUESTION # 305
You are responsible for protecting highly sensitive data in BigQuery. Your operations teams need access to this data, but given privacy regulations, you want to ensure that they cannot read the sensitive fields such as email addresses and first names. These specific sensitive fields should only be available on a need-to-know basis to the HR team. What should you do?

  • A. Perform data redaction with the DLP API and store that data in BigQuery for later use.
  • B. Perform data inspection with the DLP API and store that data in BigQuery for later use.
  • C. Perform data masking with the DLP API and store that data in BigQuery for later use.
  • D. Perform tokenization for Pseudonymization with the DLP API and store that data in BigQuery for later use.

Answer: D

Explanation:
Pseudonymization is a de-identification technique that replaces sensitive data values with cryptographically generated tokens. Pseudonymization is widely used in industries like finance and healthcare to help reduce the risk of data in use, narrow compliance scope, and minimize the exposure of sensitive data to systems while preserving data utility and accuracy.
https://cloud.google.com/dlp/docs/pseudonymization


NEW QUESTION # 306
You are tasked with exporting and auditing security logs for login activity events for Google Cloud console and API calls that modify configurations to Google Cloud resources. Your export must meet the following requirements:
Export related logs for all projects in the Google Cloud organization.
Export logs in near real-time to an external SIEM.
What should you do? (Choose two.)

  • A. Ensure that the SIEM processes the AuthenticationInfo field in the audit log entry to gather identity information.
  • B. Enable Google Workspace audit logs to be shared with Google Cloud in the Admin Console.
  • C. Enable Data Access audit logs at the organization level to apply to all projects.
  • D. Create a Log Sink at the organization level with the includeChildren parameter, and set the destination to a Pub/Sub topic.
  • E. Create a Log Sink at the organization level with a Pub/Sub destination.

Answer: B,D


NEW QUESTION # 307
......

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