[2026] Pass Google Professional-Cloud-Architect Exam in First Attempt Easily [Q157-Q173]

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[2026] Pass Google Professional-Cloud-Architect Exam in First Attempt Easily

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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Topic 1: Ensuring solution and operations reliability	– Assisting with support 1. Incident response and troubleshooting 2. Capacity planning and scaling – Monitoring/logging/profiling/alerting solution 1. Performance profiling and tracing 2. Error reporting and alerting 3. Cloud Monitoring and Cloud Logging – Deployment and release management 1. Release management and versioning 2. Deployment strategies and rollback – Evaluating quality control measures
Topic 2: Managing and provisioning a solution infrastructure	– Configuring individual storage systems 1. Cloud SQL and Spanner 2. Cloud Storage configuration 3. Datastore and Bigtable – Configuring compute systems 1. Cloud Run and Cloud Functions 2. GKE configuration and management 3. Compute Engine configuration – Configuring network topologies 1. Hybrid and multi-cloud connectivity 2. VPC design and peering 3. Private access and VPN configuration
Topic 3: Managing implementation	– Interacting with Google Cloud programmatically 1. Cloud SDK and gcloud CLI 2. APIs and client libraries 3. Infrastructure as Code (IaC) – Advising development/operation team(s) to ensure successful deployment 1. Deployment strategies 2. Application development best practices 3. Testing and validation
Topic 4: Analyzing and optimizing technical and business processes	– Analyzing and defining business processes 1. Cost management and optimization 2. Business KPIs and metrics 3. Stakeholder communication – Analyzing and defining technical processes 1. CI/CD pipeline design 2. Incident management and troubleshooting 3. Software development lifecycle (SDLC)
Topic 5: Designing and planning a cloud solution architecture	– Designing solution infrastructure that meets business requirements 1. Business use cases and product strategy 2. Data and storage architecture 3. Migration planning 4. Integration with existing systems 5. Networking and compute resources 6. Cost optimization – Designing network, storage, and compute resources 1. Compute engine selection and configuration 2. Network design including load balancing and firewall rules 3. GKE cluster and workload design 4. Storage options and data lifecycle management – Creating a migration plan 1. Identifying workloads for migration 2. Refactoring and rearchitecting applications 3. Planning data migration – Enabling future cloud technology 1. Designing for scalability and elasticity 2. Implementing future-proof architectures
Topic 6: Designing for security and compliance	– Designing for security 1. Network security 2. Identity and Access Management (IAM) 3. Application security 4. Data security and encryption – Designing for compliance 1. Data residency and sovereignty 2. Audit logging and monitoring 3. Regulatory and compliance requirements

Q157. The JencoMart security team requires that all Google Cloud Platform infrastructure is deployed using a least privilege model with separation of duties for administration between production and development resources.
What Google domain and project structure should you recommend?

Create two G Suite accounts to manage users: one for development/test/staging and one for production.
Each account should contain one project for every application

Create two G Suite accounts to manage users: one with a single project for all development applications and one with a single project for all production applications

Create a single G Suite account to manage users with each stage of each application in its own project

Create a single G Suite account to manage users with one project for the development/test/staging environment and one project for the production environment

Q158. You are deploying a PHP App Engine Standard service with SQL as the backend. You want to minimize the number of queries to the database.
What should you do?

Set the memcache service level to dedicated. Create a key from the hash of the query, and return database values from memcache before issuing a query to Cloud SQL.

Set the memcache service level to dedicated. Create a cron task that runs every minute to populate the cache with keys containing query results.

Set the memcache service level to shared. Create a cron task that runs every minute to save all expected queries to a key called “cached-queries”.

Set the memcache service level to shared. Create a key called “cached-queries”, and return database values from the key before using a query to Cloud SQL.

Q159. Case Study: 2 – TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers’ needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment

TerramEarth’s existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
– Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
– Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
– Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers’ yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I’m concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn’t take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. TerramEarth’s CTO wants to use the raw data from connected vehicles to help identify approximately when a vehicle in the field will have a catastrophic failure. You want to allow analysts to centrally query the vehicle data. Which architecture should you recommend?
A:

B:

C:

D:

Option A

Option B

Option C

Option D

Q160. You want to create a private connection between your instances on Compute Engine and your on- premises data center. You require a connection of at least 20 Gbps. You want to follow Google- recommended practices. How should you set up the connection?

Create a VPC and connect it to your on-premises data center using Dedicated Interconnect.

Create a VPC and connect it to your on-premises data center using a single Cloud VPN.

Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises data center using Dedicated Interconnect.

Create a Cloud Content Delivery Network (Cloud CDN) and connect it to your on-premises datacenter using a single Cloud VPN.

Q161. You are working at a sports association whose members range in age from 8 to 30. The association collects a large amount of health data, such as sustained injuries. You are storing this data in BigQuery. Current legislation requires you to delete such information upon request of the subject. You want to design a solution that can accommodate such a request. What should you do?

Use a unique identifier for each individual. Upon a deletion request, delete all rows from BigQuery with this identifier.

When ingesting new data in BigQuery, run the data through the Data Loss Prevention (DLP) API to identify any personal information. As part of the DLP scan, save the result to Data Catalog. Upon a deletion request, query Data Catalog to find the column with personal information.

Create a BigQuery view over the table that contains all data. Upon a deletion request, exclude the rows that affect the subject’s data from this view. Use this view instead of the source table for all analysis tasks.

Use a unique identifier for each individual. Upon a deletion request, overwrite the column with the unique identifier with a salted SHA256 of its value.

Q162. Case Study: 12 – Altostrat Media
Company Overview
Altostrat is a prominent player in the media industry, with an extensive collection of audio and video content that comprises podcasts, interviews, news broadcasts, and documentaries. Their success in delivering premium content to a diverse audience requires a content management system that can keep pace with the dynamic media landscape.
Solution Concept
Altostrat seeks to modernize its content management and user engagement strategies using Google Cloud’s generative AI. They want a platform that empowers customers with personalized recommendations, natural language interactions and seamless self-service support.
Simultaneously, they want to drive revenue growth through dynamic pricing targeted marketing, and personalized product suggestions.
The seamless integration of AI-powered tools into the existing Google Cloud environment will enable Altostrat to efficiently manage their vast media library, enhance user experiences, and unlock new revenue streams. Google Cloud’s generative AI will solidify their leadership in the media industry.
Existing Technical Environment
Altostrat’s content management and delivery platform leverages GKE for scalability and high availability, essential for handling their vast media library. Their extensive media library spanning various documents, audio and video formats is stored in Cloud Storage. To gain valuable insights into user behavior, content consumption patterns, and audience demographics, Altostrat leverages BigQuery as their primary data warehouse. Additionally, they use Cloud Run functions for serverless execution of event-driven tasks such as video transcoding metadata extraction, and personalized content recommendations.
While Altostrat has made significant strides in cloud adoption, they also maintain some legacy on- premises systems for specific workflows like content ingestion and archival. These systems are slated for modernization and migration to Google Cloud in the near future. User management and authentication are currently handled through a combination of Google Identity and third-party identity providers. For monitoring and observability, Altostrat relies on a mix of native Google Cloud tools like Cloud Monitoring and open-source solutions like Prometheus, with alerts primarily delivered via email notifications.
Business Requirements
– Accelerate and enhance the reliability of operational workflows across all environments. [Google
Cloud + On-premises]
– Simplify infrastructure management for rapid application deployment.
– Optimize cloud storage costs while maintaining high availability and scalability for media
content.
– Enable natural language interaction with the platform with 24/7 user support.
– Automatically generate concise summaries of media content.
– Extract rich metadata from media assets using NLP and computer vision.
– Detect and filter inappropriate content.
– Analyze media content to identify trends and extract insights.
– Inform content strategy and decision making with data.
Technical Requirements
– Modernize CI/CD for containerized deployments with a centralized management platform.
– Secure, high-performance hybrid cloud connectivity for data ingestion.
– Provide scalable, performant kubernetes environments both on-premises and in the cloud.
– Optimize cloud storage costs for growing media volumes.
– Design AI-powered detection of harmful content.
– Ensure that AI systems are auditable and their decisions can be explained.
– Leverage LLMs and conversational AI for personalized experiences and content virality.
– Develop advanced chatbots with natural language understanding to provide personalized
assistance.
– Automated summarization for diverse media.
Executive Statement
At Altostrat, we are embracing the next frontier of artificial intelligence to revolutionize our content strategy. By harnessing the power of generative AI, we will create an unparalleled user experience by empowering our audience with intelligent toots for content discovery, personalized recommendations, and seamless interaction. Reliability and cost management are our top priorities. This strategic initiative will deepen engagement, foster customer loyalty, and unlock new revenue streams through targeted marketing and tailored content offerings. We see a future where Al-driven innovation is central to our business, leading to greater success for our company and delivering exceptional value to our customers.
For this question, refer to the Altostrat Media case study. Altostrat stores a large library of media content, including sensitive interviews and documentaries, in Cloud Storage. They are concerned about the confidentiality of this content and want to protect it from unauthorized access. You need to implement a Google-recommended solution that is easy to integrate and provides Altostrat with control and auditability of the encryption keys. What should you do?

Configure Cloud Storage to use server-side encryption with Google-managed encryption keys.
Create a bucket policy to restrict access to only authorized Google groups and required service accounts.

Use Cloud Storage default encryption at rest. Implement fine-grained access control using IAM roles and groups to restrict access to sensitive buckets

Implement client-side encryption before uploading it to Cloud Storage. Store the encryption keys in a HashiCorp Vault instance deployed on Google Kubernetes Engine (GKE). Implement fine- grained access control to sensitive Cloud Storage buckets using IAM roles.

Use customer-managed encryption keys (CMEK) for all Cloud Storage buckets storing sensitive media content. Implement fine-grained access control using IAM roles and groups to restrict access to sensitive buckets.

Q163. You need to design a solution for global load balancing based on the URL path being requested. You need to ensure operations reliability and end-to-end in-transit encryption based on Google best practices.
What should you do?

Create a cross-region load balancer with URL Maps.

Create an HTTPS load balancer with URL maps.

Create appropriate instance groups and instances. Configure SSL proxy load balancing.

Create a global forwarding rule. Configure SSL proxy balancing.

Q164. Your company captures all web traffic data in Google Analytics 260 and stores it in BigQuery. Each country has its own dataset. Each dataset has multiple tables. You want analysts from each country to be able to see and query only the data for their respective countries.
How should you configure the access rights?

Create a group per country. Add analysts to their respective country-groups. Create a single group
‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of BigQuery jobUser. Share the appropriate dataset with view access with each respective analyst country-group.

Create a group per country. Add analysts to their respective country-groups. Create a single group
‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of BigQuery jobUser. Share the appropriate tables with view access with each respective analyst countrygroup.

Create a group per country. Add analysts to their respective country-groups. Create a single group
‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of BigQuery dataViewer. Share the appropriate dataset with view access with each respective analyst country-group.

Create a group per country. Add analysts to their respective country-groups. Create a single group
‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of BigQuery dataViewer. Share the appropriate table with view access with each respective analyst countrygroup.

Q165. For this question, refer to the Mountkirk Games case study.
Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a thorough testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?

Create a scalable environment in GCP for simulating production load.

Use the existing infrastructure to test the GCP-based backend at scale.

Build stress tests into each component of your application using resources internal to GCP to simulate load.

Create a set of static environments in GCP to test different levels of load – for example, high, medium, and low.

Q166. A news feed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed.
What is the most likely cause of this problem?

The session variable is local to just a single instance

The session variable is being overwritten in Cloud Datastore

The URL of the API needs to be modified to prevent caching

The HTTP Expires header needs to be set to -1 stop caching

Q167. You are developing an application using different microservices that should remain internal to the cluster. You want to be able to configure each microservice with a specific number of replicas.
You also want to be able to address a specific microservice from any other microservice in a uniform way, regardless of the number of replicas the microservice scales to. You need to implement this solution on Google Kubernetes Engine. What should you do?

Deploy each microservice as a Deployment.
Expose the Deployment in the cluster using a Service, and use the Service DNS name to address it from other microservices within the cluster.

Deploy each microservice as a Deployment.
Expose the Deployment in the cluster using an Ingress, and use the Ingress IP address to address the Deployment from other microservices within the cluster.

Deploy each microservice as a Pod.
Expose the Pod in the cluster using a Service, and use the Service DNS name to address the microservice from other microservices within the cluster.

Deploy each microservice as a Pod.
Expose the Pod in the cluster using an Ingress, and use the Ingress IP address name to address the Pod from other microservices within the cluster.

Q168. Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

Load data into Google BigQuery.

Insert data into Google Cloud SQL.

Put flat files into Google Cloud Storage.

Stream data into Google Cloud Datastore.

Q169. One of the developers on your team deployed their application In Google Container Engine with the Dockerfile below. They report that their application deployments are taking too long.

You want to optimize this Dockerfile for faster deployment times without adversely affecting the app’s functionality. Which two actions should you take? Choose 2 answers

Remove Python after running pip.

Remove dependencies from requirements.txt.

Use a slimmed-down base image like Alpine linux.

Use larger machine types for your Google Container Engine node pools.

Copy the source after the package dependencies (Python and pip) are installed.

Q170. For this question, refer to the Cymbal Retail case study. Cymbal wants you to design a cloud-first data storage infrastructure for the product catalog modernization project. You want to ensure efficient data access and high availability for Cymbals web application and virtual agents while minimizing operational costs. What should you do?

Use AlloyDB for structured product data, and Cloud Storage for product images

Use Spanner for the structured product data, and BigTable for product images

Use Filestore for the structured product data and Cloud Storage for product images

Use Cloud Storage for structured product data, and BigQuery for product images

Q171. For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?

Replace the existing data warehouse with BigQuery. Use table partitioning.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

Replace the existing data warehouse with BigQuery. Use federated data sources.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

Q172. For this question, refer to the JencoMart case study.
The migration of JencoMart’s application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram. You want to maximize throughput. What are three potential bottlenecks? (Choose 3 answers.)

A single VPN tunnel, which limits throughput

A tier of Google Cloud Storage that is not suited for this task

A copy command that is not suited to operate over long distances

Fewer virtual machines (VMs) in GCP than on-premises machines

A separate storage layer outside the VMs, which is not suited for this task

Complicated internet connectivity between the on-premises infrastructure and GCP

Q173. You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket. You want to optimize ongoing Cloud Storage spend. What should you do?

Write a lifecycle management rule in XML and push it to the bucket with gsutil.

Write a lifecycle management rule in JSON and push it to the bucket with gsutil.

Schedule a cron script using gsutil is -lr gs://backups/** to find and remove items older than 90 days.

Schedule a cron script using gsutil ls -1 gs://backups/** to find and remove items older than 90 days and schedule it with cron.

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Google Professional-Cloud-Architect Exam Syllabus Topics:

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