Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 4 )

Updated On: 15-Sep-2026

Your customer is moving an existing corporate application to Google Cloud Platform from an on-premises data center. The business owners require minimal user disruption. There are strict security team requirements for storing passwords.
What authentication strategy should they use?

  1. Use G Suite Password Sync to replicate passwords into Google
  2. Federate authentication via SAML 2.0 to the existing Identity Provider
  3. Provision users in Google using the Google Cloud Directory Sync tool
  4. Ask users to set their Google password to match their corporate password

Answer(s): B

Explanation:

The correct answer is B. Federate authentication via SAML 2.0 to the existing Identity Provider . Here's why:
Federated Authentication (SAML 2.0): This approach allows users to authenticate using their existing on-premises credentials managed by their existing Identity Provider (IdP). This aligns with the requirement for minimal user disruption since users continue using their familiar login process. SAML 2.0, an industry-standard protocol, securely passes authentication assertions between the IdP and Google Cloud, ensuring a seamless user experience. This method satisfies the security team's concerns about password handling because passwords remain stored in the customer's secure environment rather than being replicated to Google Cloud. Google Cloud then trusts the assertions made by the IdP, granting access based on policies.
Why other options are incorrect:

A: Use G Suite Password Sync: This option replicates passwords to Google, violating the security team's requirement of secure password storage. It also introduces potential issues during password synchronization and maintenance. C. Provision users in Google using the Google Cloud Directory Sync tool: While this option synchronizes user information (username, email, etc.) to Google, it doesn't address the need for seamless authentication with existing corporate credentials. It would still require users to create and remember a separate Google password, increasing user disruption. D. Ask users to set their Google password to match their corporate password: This is highly discouraged due to significant security risks like password reuse and difficulty enforcing strong password policies. It creates multiple attack surfaces, potentially compromising both environments.
In summary: SAML 2.0 federation provides the most secure and user-friendly authentication method by allowing Google Cloud to leverage the existing on-premises authentication infrastructure without replicating passwords and minimizing user disruption.
Authoritative Links:
Google Cloud Documentation on Federated Identities: https://cloud.google.com/architecture/identity/federated-identities SAML 2.0 Overview: https://en.wikipedia.org/wiki/Security_Assertion_Markup_Language Google Cloud Identity and Access Management (IAM) Documentation: https://cloud.google.com/iam/docs



Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options. These options include a mix of batch and stream processing, as they are running some hourly jobs and live- processing some data as it comes in.
Which technology should they use for this?

  1. Google Cloud Dataproc
  2. Google Cloud Dataflow
  3. Google Container Engine with Bigtable
  4. Google Compute Engine with Google BigQuery

Answer(s): B

Explanation:

The correct answer is B. Google Cloud Dataflow . Dataflow is a fully managed, serverless stream and batch data processing service. This makes it ideal for the scenario described, where the company needs to handle both hourly batch jobs and real-time data streaming. Dataflow uses a unified programming model that allows developers to express both types of processing within the same pipeline. This reduces development complexity and allows for code reuse between batch and streaming workloads. Options A, C, and D are less suitable: Dataproc (A) is primarily for batch processing using Hadoop and Spark, and doesn't natively handle streaming as efficiently as Dataflow. Google Container Engine (C) with Bigtable provides a platform for running containerized applications and a scalable NoSQL database but lacks the specific data processing capabilities for both batch and stream. Compute Engine (D) with BigQuery requires more manual configuration to set up both batch and streaming and doesn't provide the same level of managed service as Dataflow.
Here's a breakdown of why Dataflow is the superior choice:
Unified Programming Model: Dataflow enables both batch and stream processing using a single API, reducing the need for separate implementations. Scalability and Reliability: Dataflow is a fully managed service that automatically scales resources based on workload demands and ensures high reliability without manual intervention. Time-Windowing and Watermarks: Dataflow supports advanced windowing techniques for temporal analysis in streams and provides mechanisms for dealing with late data. Data Source and Sink Agnostic: Dataflow integrates with a wide variety of data sources and sinks, including Google Cloud Storage, Pub/Sub, and BigQuery, thus seamlessly integrating with their data infrastructure.
Further research:
Google Cloud Dataflow Documentation: https://cloud.google.com/dataflow/docs Dataflow concepts: https://cloud.google.com/dataflow/docs/concepts Batch vs Stream Processing with Dataflow: https://cloud.google.com/blog/products/data-analytics/stream-processing-with-apache-beam-and-google-cloud-dataflow



Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update.
What strategy should you take?

  1. Work with your ISP to diagnose the problem
  2. Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application
  3. Roll back to an earlier known good release initially, then use Stackdriver Trace and Logging to diagnose the problem in a development/test/staging environment
  4. Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and Logging to diagnose the problem

Answer(s): C

Explanation:

The optimal strategy is to first mitigate the impact on users by reverting to a known stable version (rollback) and then thoroughly investigating the root cause in a non-production environment. Option C aligns with this approach. Rolling back the application immediately, as suggested in option C and D, is crucial to restore service functionality and eliminate user disruption, representing the principle of minimizing impact. Analyzing the problematic deployment in a development/test/staging environment, also advocated by C, isolates the issue from production users and allows for non-disruptive troubleshooting. Using Stackdriver Trace helps identify performance bottlenecks by providing latency data for requests, while Stackdriver Logging enables the analysis of application logs for potential errors or issues after the rollback. Option A is inadequate as the issue is related to code changes and not typically an ISP problem, and troubleshooting requires access to application specifics. Option B suggests contacting support without immediate mitigation, which is less optimal than a rapid rollback to restore the application. Rerolling immediately at a quiet period as in option D is risky without analysis of the root cause; the issue could simply reappear.
Justification Summary:
1. Minimize Impact: Rollback quickly to reduce user impact. 2. Non-disruptive Investigation: Analyze the problematic version in a separate environment. 3. Diagnostic Tools: Utilize Stackdriver Trace for latency and Stackdriver Logging for error analysis. 4. Root Cause Analysis: The issue stems from code changes, not an ISP or network issues. 5. Avoid Rerolling Without Analysis: Avoid deploying the same problematic code again before investigation.
Authoritative Links:
Google Cloud Documentation - Rollback app deployments: https://cloud.google.com/appengine/docs/standard/python/deploying-and-managing-versions Google Cloud Documentation - Stackdriver Trace: https://cloud.google.com/trace/docs Google Cloud Documentation - Stackdriver Logging: https://cloud.google.com/logging/docs



A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space. How can you remediate the problem with the least amount of downtime?

  1. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.
  2. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine
  3. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux
  4. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk
  5. In the Cloud Platform Console, create a snapshot of the persistent disk restore the snapshot to a new larger disk, unmount the old disk, mount the new disk and restart the database service

Answer(s): A

Explanation:

Option A is the correct solution because it leverages the ability to dynamically resize persistent disks on Google Compute Engine without requiring a complete shutdown of the virtual machine. This minimizes downtime, which is crucial for production database systems. First, you can increase the persistent disk's size directly through the Google Cloud Platform Console. After the size increase, the underlying storage is expanded. However, the operating system still sees the original partition size. The resize2fs command in Linux allows you to expand the ext4 file system to utilize the newly allocated disk space without unmounting the volume. This is an in-place operation and significantly reduces downtime. Options B, C, D, and E introduce more downtime than option A. Option B requires a full VM shutdown and restart. Option C misses the essential resize2fs step. Option D involves creating, formatting, and migrating data to a new disk which requires significant downtime and complexity. Option E involves the overhead of snapshot creation and restoration to another disk. This also involves switching disks which is another disruptive process. Therefore, Option A is the most efficient approach to expanding the disk space with minimal downtime and aligns with best practices for operational efficiency and availability in cloud environments.
Authoritative links for further research:
Resizing persistent disks: https://cloud.google.com/compute/docs/disks/add-persistent-disk#resize_pd resize2fs command: https://man7.org/linux/man-pages/man8/resize2fs.8.html



Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

  1. Create a tokenizer service and store only tokenized data
  2. Create separate projects that only process credit card data
  3. Create separate subnetworks and isolate the components that process credit card data
  4. Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data
  5. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor

Answer(s): A

Explanation:

The correct answer is A. Create a tokenizer service and store only tokenized data.
This approach minimizes the scope of PCI compliance by avoiding the storage of actual credit card numbers (PANs). Tokenization replaces sensitive PANs with non-sensitive, randomly generated values, rendering the original data useless to unauthorized individuals. The tokenized data can then be freely used for analysis and reporting, including in broader data processing environments, without triggering PCI DSS requirements. This segregation of sensitive data handling to a tokenizer service significantly reduces the PCI scope by isolating the sensitive credit card information from downstream analytics systems. Using tokens allows for analysis on transaction trends without the actual card data being present, fulfilling the business requirement while significantly reducing risk. Options B, C, and D don't actively reduce the scope of PCI compliance; instead, they simply isolate, separate, or label infrastructure. Option E, while essential for auditing, doesn't shrink the scope. Tokenization addresses compliance by fundamentally removing sensitive data.
Authoritative Links:
PCI Security Standards Council: Tokenization Guidelines: https://www.pcisecuritystandards.org/document_library (Search for "Tokenization Guidelines") - This is the primary source for understanding PCI tokenization requirements. Google Cloud Compliance: https://cloud.google.com/security/compliance/ - Overview of Google Cloud's compliance programs, which includes PCI. Google Cloud Security: https://cloud.google.com/security - Provides security best practices on Google Cloud, including data protection.



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