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Google Professional-Cloud-Security-Engineer Certification Exam is designed for individuals who are interested in showcasing their expertise in managing and securing applications on the Google Cloud Platform. Google Cloud Certified - Professional Cloud Security Engineer Exam certification is ideal for professionals who are responsible for implementing security controls and maintaining compliance for cloud-based solutions. With this certification, individuals can demonstrate their skills in designing and implementing secure infrastructure, configuring security policies, and managing compliance controls on the Google Cloud Platform.
Google Professional-Cloud-Security-Engineer Exam is a certification offered by Google for professionals who are responsible for ensuring the security of data and infrastructure in the cloud. Professional-Cloud-Security-Engineer exam is designed to test the candidate’s knowledge and skills in implementing security controls and maintaining compliance in the Google Cloud Platform (GCP). Google Cloud Certified - Professional Cloud Security Engineer Exam certification is intended for security engineers, security architects, and other professionals who have experience in cloud security.
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Google Professional-Cloud-Security-Engineer Certification Exam consists of multiple-choice questions and is designed to test the candidate's knowledge and skills in securing cloud-based solutions. Professional-Cloud-Security-Engineer exam covers a wide range of topics, including cloud security fundamentals, identity and access management, network security, data protection, and compliance. Candidates are required to have a deep understanding of Google Cloud Platform services and be able to apply their knowledge to real-world scenarios. Google Cloud Certified - Professional Cloud Security Engineer Exam certification exam is a great way for professionals to validate their skills in cloud security and gain recognition for their expertise in the field.
NEW QUESTION # 258
You discovered that sensitive personally identifiable information (PII) is being ingested to your Google Cloud environment in the daily ETL process from an on-premises environment to your BigQuery datasets. You need to redact this data to obfuscate the PII, but need to re-identify it for data analytics purposes. Which components should you use in your solution? (Choose two.)
Answer: B,D
NEW QUESTION # 259
A security audit uncovered several inconsistencies in your project's Identity and Access Management (IAM) configuration. Some service accounts have overly permissive roles, and a few external collaborators have more access than necessary. You need to gain detailed visibility into changes to IAM policies, user activity, service account behavior, and access to sensitive projects. What should you do?
Answer: B
Explanation:
The problem requires gaining "detailed visibility into changes to IAM policies, user activity, service account behavior, and access to sensitive projects" due to security inconsistencies.
Cloud Audit Logs: Cloud Audit Logs records administrative activities, data access, and system events across Google Cloud. These logs are the primary source of truth for tracking "who did what, where, and when" in your Google Cloud environment.
Extract Reference: "Cloud Audit Logs maintains the following audit logs for each project, folder, and organization: Admin Activity audit logs, Data Access audit logs, System Event audit logs, Policy Denied audit logs." Extract Reference: "Admin Activity audit logs contain log entries for API calls or other actions that modify the configuration or metadata of resources. Data Access audit logs record API calls that read the configuration or metadata of resources, as well as user-provided data." (Google Cloud Documentation: "Cloud Audit Logs overview" - https://cloud.google.com/logging/docs/audit) These logs directly capture:Changes to IAM policies: Recorded in Admin Activity logs.
User activity: Recorded in Admin Activity and Data Access logs.
Service account behavior: Actions performed by service accounts are logged in the same way as user actions.
Access to sensitive projects: Data Access logs, especially for sensitive data services, record access events.
Log Export Sinks: To gain "detailed visibility" and enable "correlation with other event sources," these audit logs should be exported to a centralized Security Information and Event Management (SIEM) solution. Log sinks allow you to route logs from Cloud Logging to various destinations, including BigQuery, Cloud Storage, or Pub/Sub (which can then feed into a SIEM).
Extract Reference: "You can use sinks to route some or all of your logs to supported destinations." and "Many security information and event management (SIEM) systems can ingest logs through Cloud Pub/Sub." (Google Cloud Documentation: "Routing and storage overview | Cloud Logging" - https://cloud.google.com
/logging/docs/routing-overview)
Let's evaluate the other options:
A). OS Config Management agent: This service manages operating system configurations, patching, and inventory on VMs. It is not designed to monitor or log IAM policy changes, user activity, or service account behavior within Google Cloud's IAM system.
B). Metrics Explorer in Cloud Monitoring: While Cloud Monitoring can provide some metrics related to service account authentication, it focuses on time-series data and operational health metrics. It does not provide the detailed, event-level audit records necessary for forensic analysis of IAM policy changes, specific user actions, or granular access events to sensitive data that Cloud Audit Logs offer.
D). Cloud Functions triggered by IAM policy changes + Policy Simulator: This describes a reactive automation pattern for some IAM changes. While useful for immediate alerting on risky modifications, it's a custom solution for a subset of the requirements. It doesn't inherently provide "detailed visibility" into all user activity or comprehensive service account behavior across all projects, nor does it replace the robust logging and correlation capabilities of a SIEM solution ingesting raw audit logs. Cloud Audit Logs are the fundamental data source this approach would rely on.
Therefore, leveraging Cloud Audit Logs and exporting them to a SIEM is the most comprehensive and recommended approach for gaining detailed visibility into IAM-related changes and activities across your Google Cloud organization.
NEW QUESTION # 260
You are deploying regulated workloads on Google Cloud. The regulation has data residency and data access requirements. It also requires that support is provided from the same geographical location as where the data resides.
What should you do?
Answer: C
Explanation:
Assured Workloads for Google Cloud allows you to deploy regulated workloads with data residency, access, and support requirements. It helps you configure your environment in a manner that aligns with specific compliance frameworks and standards.
NEW QUESTION # 261
Your company's users access data in a BigQuery table. You want to ensure they can only access the data during working hours.
What should you do?
Answer: B
Explanation:
o ensure that users can only access the data in a BigQuery table during working hours, you can assign the BigQuery Data Viewer role with an IAM condition that specifies the allowed access times. This method leverages IAM Conditions, which allow you to define and enforce time-based access policies. Here's how to do it:
Identify the BigQuery Table: Determine which BigQuery table(s) require restricted access.
Create an IAM Policy with Conditions: Define an IAM policy that includes a condition for time-based access.
You can do this using the Google Cloud Console, gcloud command-line tool, or directly editing the IAM policy JSON.
Specify Working Hours: In the IAM condition, specify the time frame during which access is allowed. For example, you can set access to be allowed from 9 AM to 5 PM on weekdays.
Assign the Role with Conditions: Apply the policy to the users or groups who need access. Ensure that the condition is correctly attached to the BigQuery Data Viewer role.
Example using gcloud:
gcloud projects add-iam-policy-binding [PROJECT_ID] \
--member=user:[USER_EMAIL] \
--role=roles/bigquery.dataViewer \
--condition=expression="(request.time.getFullYear() == 2024) && (request.time.getDayOfWeek() in [1, 2, 3,
4, 5]) && (request.time.getHours() >= 9) && (request.time.getHours() < 17)",title="Working hours condition",description="Access limited to working hours" References Google Cloud IAM Conditions Google Cloud BigQuery IAM Roles
NEW QUESTION # 262
You have noticed an increased number of phishing attacks across your enterprise user accounts. You want to implement the Google 2-Step Verification (2SV) option that uses a cryptographic signature to authenticate a user and verify the URL of the login page. Which Google 2SV option should you use?
Answer: D
NEW QUESTION # 263
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