Free PDF 2025 Google First-grade Professional-Cloud-Security-Engineer: Test Google Cloud Certified - Professional Cloud Security Engineer Exam Vce Free

P.S. Free & New Professional-Cloud-Security-Engineer dumps are available on Google Drive shared by Prep4SureReview: https://drive.google.com/open?id=153A416frywS8nR01wEC1eiG-w-QA-_RP

Prep4SureReview is working on providing most helpful the real test questions answer in certification exams many years especially for Professional-Cloud-Security-Engineer. It provide 100% real test exam materials to help you clear exam surely. If you find some mistakes in other sites, you will know how the important the site have certain power. Choosing good Google Professional-Cloud-Security-Engineer Exam Materials, we will be your only option.

Google Professional-Cloud-Security-Engineer Exam is a challenging and valuable certification for cloud security professionals and engineers. It measures the candidate's ability to design and implement secure Google Cloud Platform solutions and provides opportunities for career advancement and higher salaries. Google Cloud Certified - Professional Cloud Security Engineer Exam certification is recognized by industry leaders and helps organizations to identify skilled and knowledgeable cloud security professionals and engineers.

>> Test Professional-Cloud-Security-Engineer Vce Free <<

Test Professional-Cloud-Security-Engineer Registration, Reliable Professional-Cloud-Security-Engineer Test Pattern

Do you want to pass Professional-Cloud-Security-Engineer exam easily? Professional-Cloud-Security-Engineer exam training materials of Prep4SureReview is a good choice, which covers all the content and answers about Professional-Cloud-Security-Engineer exam dumps you need to know. Then you can master the difficult points in a limited time, pass the Professional-Cloud-Security-Engineer Exam in one time, improve your professional value and stand more closely to success.

Google Professional-Cloud-Security-Engineer Certification is an excellent way for IT professionals to demonstrate their skills and expertise in cloud security. It is also a valuable credential for organizations that use GCP, as it ensures that their security professionals have the knowledge and skills needed to secure cloud-based infrastructure effectively.

Google Cloud Certified - Professional Cloud Security Engineer Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
Your organization has 3 TB of information in BigQuery and Cloud SQL You need to develop a cost-effective, scalable, and secure strategy to anonymize the personally identifiable information (PII) that exists today What should you do?

A. Inspect a representative sample of the data in BigQuery and Cloud SQL to identify PII Based on this analysis, develop a custom script to anonymize the identified PII
B. Create a new BigQuery dataset and Cloud SQL instance Copy a small subset of the data to these new locations Use Cloud Data Loss Prevention API to scan this subset for PII Based on the results, create a custom anonymization script and apply the script to the entire 3 TB dataset in the original locations
C. Export all 3TB of data from BigQuery and Cloud SQL to Cloud Storage Use Cloud Sensitive Data Protection to anonymize the exported data Re-import the anonymized data back into BigQuery and Cloud SQL
D. Scan your BigQuery and Cloud SQL data using the Cloud DLP data profiling feature Use the data profiling results to create a de-identification strategy with either Cloud Sensitive Data Protection's de-identification templates or custom configurations

Answer: D

Explanation:
The problem requires a cost-effective, scalable, and secure strategy to anonymize 3 TB of PII in BigQuery and Cloud SQL Cloud Sensitive Data Protection (Cloud DLP): Cloud DLP is Google Cloud's fully managed service designed for discovering, classifying, and de-identifying sensitive data, including PII, across various Google Cloud services and on-premises environments It is specifically built for scale and security Extract Reference: "Sensitive Data Protection helps you discover, classify, and de-identify sensitive data" and "Sensitive Data Protection includes advanced de-identification techniques, such as tokenization, masking, and format-preserving encryption, to help you protect your sensitive data while preserving its utility for analysis" (Google Cloud Documentation: "Sensitive Data Protection overview" - https://cloudgooglecom/sensitive-data-protection/docs/overview) Data Profiling: Cloud DLP's data profiling feature automatically scans your data (eg, BigQuery tables, Cloud SQL databases) to identify where sensitive data resides and to understand its characteristics This is crucial for developing an effective de-identification strategy Extract Reference: "Data profiles provide metrics and insights about your sensitive and high-risk data, helping you make informed decisions about data protection, access, and storage" and "When you configure Sensitive Data Protection to profile data in BigQuery, Cloud Storage, and Datastore, it automatically scans your data for sensitive information" (Google Cloud Documentation: "About data profiles" - https://cloudgooglecom/sensitive-data-protection/docs/concepts-data-profiles) De-identification Templates and Custom Configurations: After profiling identifies the PII, Cloud DLP offers various de-identification methods, which can be applied using pre-built templates or custom configurations This allows for flexible and targeted anonymization For BigQuery specifically, DLP can integrate with remote functions for de-identification at query time, minimizing data movement Extract Reference: "De-identification techniques, like encryption, obfuscate raw sensitive identifiers in your data These techniques let you preserve the utility of your data for joining or analytics, while reducing the risk of handling the data" and "You can use this tutorial to replace that pipeline with a SQL query for only re-identification or both de-identification and re-identification" (Google Cloud Documentation: "De-identify BigQuery data at query time | Sensitive Data Protection Documentation" - https://cloudgooglecom/sensitive-data-protection/docs/deidentify-bq-tutorial) Let's evaluate the other options:
B Copy small subset create a custom anonymization script apply the script to the entire 3 TB dataset: Relying on a "custom anonymization script" for 3 TB of data is generally not scalable, cost-effective, or secure compared to a managed service like Cloud DLP Custom scripts require significant development, testing, maintenance, and robust error handling for large datasets, and might introduce security vulnerabilities C Export all 3TB of data to Cloud Storage anonymize Re-import: While feasible, exporting and re-importing 3 TB of data is a very time-consuming and potentially costly process due to data transfer and storage operations Cloud DLP can often process data in-place or integrate more efficiently, especially with BigQuery This option might not be the most cost-effective or efficient D Inspect a representative sample develop a custom script: Similar to option B, this relies on a custom script, which is less ideal for scalability, cost-effectiveness, and security than a managed service like Cloud DLP for 3 TB of sensitive data Sample inspection is a good initial step, but the subsequent custom script for anonymization is the weak point

NEW QUESTION # 40
Your DevOps team uses Packer to build Compute Engine images by using this process:
1 Create an ephemeral Compute Engine VM.
2 Copy a binary from a Cloud Storage bucket to the VM's file system.
3 Update the VM's package manager.
4 Install external packages from the internet onto the VM.
Your security team just enabled the organizational policy. consrraints/compure.vnExtemallpAccess. to restrict the usage of public IP Addresses on VMs. In response your DevOps team updated their scripts to remove public IP addresses on the Compute Engine VMs however the build pipeline is failing due to connectivity issues.
What should you do?
Choose 2 answers

A. Enable Private Google Access on the subnet that the Compute Engine VM is deployed within.
B. Provision a Cloud VPN tunnel in the same VPC and region as the Compute Engine VM.
C. Update the VPC routes to allow traffic to and from the internet.
D. Provision a Cloud NAT instance in the same VPC and region as the Compute Engine VM
E. Provision an HTTP load balancer with the VM in an unmanaged instance group to allow inbound connections from the internet to your VM.

Answer: A,D

Explanation:
Provision a Cloud NAT Instance:
Cloud NAT (Network Address Translation) allows instances without external IP addresses to access the internet securely.
In the Google Cloud Console, navigate to the VPC Network section and select Cloud NAT.
Create a new Cloud NAT configuration, specifying the VPC and region where your Compute Engine VMs are deployed.
Configure Cloud NAT:
Ensure that the Cloud NAT instance is configured to provide outbound internet connectivity for the VMs in your specified subnet.
This setup allows the VMs to access the internet for package updates and external installations without requiring public IP addresses.
Enable Private Google Access:
Private Google Access allows VMs in a subnet to reach Google APIs and services using internal IP addresses.
In the Google Cloud Console, navigate to the VPC Network section and select Subnets.
Edit the subnet used by your Compute Engine VMs and enable Private Google Access.
Update DevOps Scripts:
Ensure that your DevOps scripts are updated to work with the new network configuration.
Test the build process to confirm that the VMs can access necessary resources and complete the build pipeline successfully.
Reference:
Cloud NAT Documentation
Private Google Access

NEW QUESTION # 41
Your organization has implemented synchronization and SAML federation between Cloud Identity and Microsoft Active Directory. You want to reduce the risk of Google Cloud user accounts being compromised. What should you do?

A. Create an Active Directory domain password policy with strong password settings, and configure post-SSO (single sign-on) 2-Step Verification with verification codes via text or phone call in the Google Admin console.
B. Create a Cloud Identity password policy with strong password settings, and configure 2-Step Verification with verification codes via text or phone call in the Google Admin console.
C. Create an Active Directory domain password policy with strong password settings, and configure post-SSO (single sign-on) 2-Step Verification with security keys in the Google Admin console.
D. Create a Cloud Identity password policy with strong password settings, and configure 2-Step Verification with security keys in the Google Admin console.

Answer: C

Explanation:
Objective: Reduce the risk of Google Cloud user accounts being compromised.
Solution: Implement strong password policies and post-SSO 2-Step Verification using security keys.
Steps:
Step 1: In Active Directory, configure a domain password policy with strong settings (e.g., complexity, length, expiration).
Step 2: In the Google Admin console, navigate to the Security settings.
Step 3: Enable 2-Step Verification and configure it to use security keys for post-SSO verification.
Step 4: Ensure all users enroll in the 2-Step Verification with security keys.
Using strong password policies in Active Directory along with security keys for 2-Step Verification post-SSO provides enhanced security against account compromises.
Reference:
Active Directory Password Policies
Google Admin Console 2-Step Verification

NEW QUESTION # 42
In a shared security responsibility model for IaaS, which two layers of the stack does the customer share responsibility for? (Choose two.)

A. Storage Encryption
B. Boot
C. Hardware
D. Network Security
E. Access Policies

Answer: A,E

NEW QUESTION # 43
Your company wants to determine what products they can build to help customers improve their credit scores depending on their age range. To achieve this, you need tojoin user information in the company's banking app with customers' credit score data received from a third party. While using this raw data will allow you to complete this task, it exposes sensitive data, which could be propagated into new systems.
This risk needs to be addressed using de-identification and tokenization with Cloud Data Loss Prevention while maintaining the referential integrity across the database. Which cryptographic token format should you use to meet these requirements?

A. Format-preserving encryption
B. Deterministic encryption
C. Secure, key-based hashes
D. Cryptographic hashing

Answer: B

Explanation:
Explanation
"This encryption method is reversible, which helps to maintain referential integrity across your database and has no character-set limitations."https://cloud.google.com/blog/products/identity-security/take-charge-of-your-data-how-tokenization
https://cloud.google.com/dlp/docs/pseudonymization
FPE provides fewer security guarantees compared to other deterministic encryption methods such as AES-SIV. For these reasons, Google strongly recommends using deterministic encryption with AES-SIV instead of FPE for all security sensitive use cases. Other methods like deterministic encryption using AES-SIV provide these stronger security guarantees and are recommended for tokenization use cases unless length and character set preservation are strict requirements-for example, for backward compatibility with a legacy data system.

NEW QUESTION # 44
......

Test Professional-Cloud-Security-Engineer Registration: https://www.prep4surereview.com/Professional-Cloud-Security-Engineer-latest-braindumps.html

BTW, DOWNLOAD part of Prep4SureReview Professional-Cloud-Security-Engineer dumps from Cloud Storage: https://drive.google.com/open?id=153A416frywS8nR01wEC1eiG-w-QA-_RP