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Google Professional-Cloud-Security-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Configuring Access25%- Designing access control
  • 1. Resource hierarchy and organization policies
  • 2. IAM roles, permissions, and policies
  • 3. Identity federation and workload identity
- Implementing access management
  • 1. User and group management
  • 2. Service accounts and key management
  • 3. Deny policies and conditional access
Topic 2: Supporting Compliance Requirements11%- Audit and assessment
  • 1. Security assessment frameworks
  • 2. Evidence collection and reporting
- Regulatory compliance
  • 1. Controls for GDPR, HIPAA, PCI DSS, ISO 27001
  • 2. Shared responsibility model
Topic 3: Ensuring Data Protection23%- Data classification and lifecycle
  • 1. Retention and deletion policies
  • 2. Sensitive data discovery and classification
- Encryption implementation
  • 1. Data loss prevention (DLP)
  • 2. Key management and rotation
  • 3. Encryption at rest (CMEK, Google-managed keys)
Topic 4: Configuring Network Security20%- Secure communication
  • 1. Encryption in transit
  • 2. Load balancer security
  • 3. Certificate management
- Perimeter security
  • 1. Identity-Aware Proxy (IAP)
  • 2. Cloud NGFW rules and policies
  • 3. VPC design and private access
Topic 5: Managing Operations19%- Security monitoring and logging
  • 1. Cloud Audit Logs and logging configuration
  • 2. Security Command Center (SCC)
  • 3. Threat detection and response
- Security automation and governance
  • 1. Infrastructure as Code security
  • 2. Policy enforcement and compliance monitoring
  • 3. Binary Authorization and supply chain security

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Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
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?

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 # 30
What is the best course of action regarding data privacy and model tuning when using Vertex AI?

Answer: B

Explanation:
When using Google Cloud's generative AI platforms like Vertex AI for model tuning, the contractual and technical safeguards are designed to protect customer data from being used for public model training.
Google's policy is that the content (data) uploaded for model tuning is used only for that specific, express purpose (tuning your model) and is not used to tune Google's foundational public models or shared with other customers.
Extracts:
"Google only uses content that you import or upload to our model tuning feature for that express purpose." (Source 6.1)
"Tuning content may be retained in connection with your tuned models for purposes of re-tuning when supported models change. When you delete a tuned model, the related tuning content is also deleted." (Source
6.1)
Therefore, based on the documented commitment for Vertex AI model tuning, no additional action is required (Do nothing) to prevent Google from using the data for general model tuning.


NEW QUESTION # 31
A company is running workloads in a dedicated server room. They must only be accessed from within the private company network. You need to connect to these workloads from Compute Engine instances within a Google Cloud Platform project.
Which two approaches can you take to meet the requirements? (Choose two.)

Answer: B,E

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/secure-data-workloads-use-cases


NEW QUESTION # 32
You have been tasked with implementing external web application protection against common web application attacks for a public application on Google Cloud. You want to validate these policy changes before they are enforced. What service should you use?

Answer: A

Explanation:
* Objective: Implement external web application protection and validate policy changes before enforcement.
* Solution: Use Google Cloud Armor's preconfigured rules in preview mode.
* Steps:
* Step 1: Open the Google Cloud Console.
* Step 2: Navigate to the Google Cloud Armor section.
* Step 3: Create or select a security policy.
* Step 4: Apply preconfigured rules to the policy.
* Step 5: Enable preview mode to simulate the effects of the rules without enforcing them.
* Step 6: Monitor the logs to validate the policy changes.
Google Cloud Armor's preview mode allows you to test and validate the impact of security policies on your application traffic before applying them, ensuring that they work as intended without disrupting the service.
References:
* Google Cloud Armor Documentation
* Using Preview Mode


NEW QUESTION # 33
As adoption of the Cloud Data Loss Prevention (DLP) API grows within the company, you need to optimize usage to reduce cost. DLP target data is stored in Cloud Storage and BigQuery. The location and region are identified as a suffix in the resource name.
Which cost reduction options should you recommend?

Answer: B

Explanation:
Objective: Optimize the usage of Cloud Data Loss Prevention (DLP) API to reduce costs.
Solution:
rowsLimit and bytesLimitPerFile: These parameters help in sampling data instead of scanning the entire dataset, thereby reducing the amount of data processed.
CloudStorageRegexFileSet: This feature allows you to specify a subset of files to be scanned using regular expressions, limiting the scope and volume of data scanned.
Steps:
Step 1: Set appropriate rowsLimit values for BigQuery data scans to sample rows instead of scanning entire tables.
Step 2: Set bytesLimitPerFile values for Cloud Storage buckets to limit the number of bytes scanned per file.
Step 3: Use CloudStorageRegexFileSet to specify the subset of files to be scanned based on patterns that match the filenames.
By combining these strategies, you effectively reduce the scope and volume of data processed by the DLP API, leading to cost savings.
Reference:
DLP API Best Practices
Configuring Finding Limits


NEW QUESTION # 34
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