Professional Cloud Architect Practice Exam — Google Certification
QuestionQ1
Designing for security and compliance
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Your company has acquired a healthcare startup and must retain its customers’ medical information for up to 4 additional years, depending on when the information was created. Corporate policy requires securely retaining this data and deleting it as soon as regulations permit.
Which approach should you use?
AStore the data in Google Drive and manually delete records as they expire.
BAnonymize the data using the Cloud Data Loss Prevention API and store it indefinitely.
CStore the data in Cloud Storage and use lifecycle management to delete files when they expire.
DStore the data in Cloud Storage and run a nightly batch script that deletes all expired data.
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QuestionQ2
Analyzing and optimizing technical and business processes
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QuestionQ3
Designing and planning a cloud solution architecture
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QuestionQ4
Ensuring solution and operations excellence
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QuestionQ5
Ensuring solution and operations excellence
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Designing and planning a cloud solution architectureManaging and provisioning a cloud solution infrastructureDesigning for security and complianceAnalyzing and optimizing technical and business processesManaging implementationEnsuring solution and operations excellence
You analyzed TerramEarth's business requirement to reduce downtime and found that most time savings can be achieved by reducing customers' wait time for parts. You decided to focus on reducing the 3-week aggregate reporting time.
Which changes to the company's processes should you recommend?
AMigrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics
BMigrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics
CIncrease fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics
DMigrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor
You have an application that will run on Compute Engine. You must design an architecture that accounts for a disaster recovery plan requiring the application to fail over to another region if a regional outage occurs. What should you do?
ADeploy the application on two Compute Engine instances in the same project but in a different region. Use the first instance to serve traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
BDeploy the application on a Compute Engine instance. Use the instance to serve traffic, and use the HTTP load balancing service to fail over to an instance on your premises in case of a disaster.
CDeploy the application on two Compute Engine instance groups, each in the same project but in a different region. Use the first instance group to serve traffic, and use the HTTP load balancing service to fail over to the standby instance group in case of a disaster.
DDeploy the application on two Compute Engine instance groups, each in a separate project and a different region. Use the first instance group to serve traffic, and use the HTTP load balancing service to fail over to the standby instance group in case of a disaster.
You need to create procedures for testing a disaster plan for a mission-critical application. You want to follow Google-recommended practices and use native GCP capabilities.
What should you do?
AUse Deployment Manager to automate service provisioning. Use Activity Logs to monitor and debug your tests.
BUse Deployment Manager to automate service provisioning. Use Stackdriver to monitor and debug your tests.
CUse gcloud scripts to automate service provisioning. Use Activity Logs to monitor and debug your tests.
DUse gcloud scripts to automate service provisioning. Use Stackdriver to monitor and debug your tests.
You have a Compute Engine application that should autoscale when total memory utilization exceeds 80%. You installed the Cloud Monitoring agent and configured the autoscaling policy as follows:
The application does not scale during high load. You want to fix this. What should you do?
AChange the Target type to DELTA_PER_MINUTE.
BChange the Metric identifier to agent.googleapis.com/memory/bytes_used.
CChange the filter to metric.label.state = 'used' AND metric.label.state = 'buffered' AND metric.label.state = 'cached' AND metric.label.state = 'slab'.
DChange the filter to metric.label.state = 'free' and the Target utilization to 20.
QuestionQ6
Designing for security and compliance
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QuestionQ7
Designing and planning a cloud solution architecture
QuestionQ8
Ensuring solution and operations excellence
QuestionQ9
Ensuring solution and operations excellence
QuestionQ10
Ensuring solution and operations excellence
QuestionQ11
Designing for security and compliance
QuestionQ12
Designing for security and compliance
QuestionQ13
Analyzing and optimizing technical and business processes
QuestionQ14
Ensuring solution and operations excellence
QuestionQ15
Designing and planning a cloud solution architecture
QuestionQ16
Designing for security and compliance
QuestionQ17
Managing and provisioning a cloud solution infrastructure
QuestionQ18
Designing for security and compliance
QuestionQ19
Designing and planning a cloud solution architecture
QuestionQ20
Ensuring solution and operations excellence
QuestionQ21
Managing and provisioning a cloud solution infrastructure
QuestionQ22
Designing and planning a cloud solution architecture
QuestionQ23
Analyzing and optimizing technical and business processes
QuestionQ24
Managing and provisioning a cloud solution infrastructure
QuestionQ25
Managing and provisioning a cloud solution infrastructure
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Mountkirk Games wants to restrict the physical locations of resources to its operating Google Cloud regions. What should you do?
AConfigure an organizational policy which constrains where resources can be deployed.
BConfigure IAM conditions to limit what resources can be configured.
CConfigure the quotas for resources in the regions not being used to 0.
DConfigure a custom alert in Cloud Monitoring so you can disable resources as they are created in other regions.
Your company intends 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 using a SQL interface.
How should you store the data to optimize it for ease of analysis?
ALoad data into Google BigQuery
BInsert data into Google Cloud SQL
CPut flat files into Google Cloud Storage
DStream data into Google Cloud Datastore
You have a Compute Engine managed instance group that adds and removes Compute Engine instances in response to your application's load. The instances use a shutdown script to remove REDIS database entries associated with each instance. You notice that many database entries were not removed and suspect the shutdown script is the cause.
You must ensure the commands in the shutdown script execute reliably whenever an instance is shut down. You create a Cloud Function to remove the database entries. What should you do next?
AModify the shutdown script to wait for 30 seconds before triggering the Cloud Function.
BDo not use the Cloud Function. Modify the shutdown script to restart if it has not completed in 30 seconds.
CSet up a Cloud Monitoring sink that triggers the Cloud Function after an instance removal log message arrives in Cloud Logging.
DModify the shutdown script to wait for 30 seconds and then publish a message to a Pub/Sub queue.
You run a cluster on Kubernetes Engine (GKE) that serves a web application. Users report that a particular part of the application no longer responds. You observe that every pod in your deployment restarts after 2 seconds. The application writes logs to standard output. You need to inspect the logs to determine the cause of the issue. Which approach can you use?
AReview the Stackdriver logs for each Compute Engine instance that is serving as a node in the cluster.
BReview the Stackdriver logs for the specific GKE container that is serving the unresponsive part of the application.
CConnect to the cluster using gcloud credentials and connect to a container in one of the pods to read the logs.
DReview the Serial Port logs for each Compute Engine instance that is serving as a node in the cluster.
You need to ensure application and operational reliability by enabling dependable task scheduling for compute on GCP. Following Google best practices, what should you do?
AUsing the Cron service provided by App Engine, publish messages directly to a message-processing utility service running on Compute Engine instances.
BUsing the Cron service provided by App Engine, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.
CUsing the Cron service provided by Google Kubernetes Engine (GKE), publish messages directly to a message-processing utility service running on Compute Engine instances.
DUsing the Cron service provided by GKE, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.
Refer to the KnightMotives Automotive case study. KnightMotives wants to personalize its dealer experience for customers and has chosen to train its own AI models for personalized recommendations. The company will begin collecting personally identifiable information (PII) from customers for use as training data for the models. KnightMotives wants to ensure maximum security and worldwide compliance. You must ensure that the data is encrypted both at rest and while AI model training occurs, without affecting model accuracy. What should you do?
AStore the training data in BigQuery using column-level encryption. Train the model using Confidential GKE Nodes.
BStore the training data in BigQuery using column-level encryption Train the model on VertexAI notebooks using customer-managed encryption keys.
CProcess all data with Sensitive Data Protection’s de-identification service. Replace any PII with a random string before storing it. Train the model using Confidential GKE Nodes.
DProcess all data with Sensitive Data Protection's de-identification service. Replace any PII with a random string before storing it. Train the model on VertexAI notebooks using customer-managed encryption keys.
Refer to the Helicopter Racing League (HRL) case study. Your team is responsible for creating a payment-card data vault for card numbers used to bill tens of thousands of viewers, merchandise consumers, and season ticket holders. You must implement a custom card-tokenization service that meets these requirements:
It must deliver low latency at minimal cost.
It must identify duplicate credit cards and must not retain plaintext card numbers.
It should support annual key rotation.
Which storage approach should you use for the tokenization service?
AStore the card data in Secret Manager after running a query to identify duplicates.
BEncrypt the card data with a deterministic algorithm stored in Firestore using Datastore mode.
CEncrypt the card data with a deterministic algorithm and shard it across multiple Memorystore instances.
DUse column-level encryption to store the data in Cloud SQL.
You need to migrate Hadoop jobs for your company’s Data Science team without changing the underlying infrastructure. You want to minimize costs and the effort required to manage infrastructure. What should you do?
ACreate a Dataproc cluster using standard worker instances.
BCreate a Dataproc cluster using preemptible worker instances.
CManually deploy a Hadoop cluster on Compute Engine using standard instances.
DManually deploy a Hadoop cluster on Compute Engine using preemptible instances.
Mountkirk Games wants to establish a continuous-delivery pipeline. Its architecture contains many small services that it needs to update and roll back quickly. Mountkirk Games has these requirements:
Services are deployed redundantly across multiple regions in the US and Europe.
Only frontend services are exposed on the public internet.
They can provide a single frontend IP for their fleet of services.
Deployment artifacts are immutable.
Which product set should they use?
AGoogle Cloud Storage, Google Cloud Dataflow, Google Compute Engine
BGoogle Cloud Storage, Google App Engine, Google Network Load Balancer
CGoogle Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer
DGoogle Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager
Company Overview —
KnightMotives is a car manufacturer that specializes in autonomous, self-driving vehicles, including Battery Electric Vehicles (BEVs), hybrids, and traditional internal combustion engine (ICE) vehicles. Although KnightMotives has advanced the in-vehicle experience in its BEV fleet, the hybrid and ICE vehicles have not yet adopted these new systems and are regarded poorly by critics and drivers. The absence of modern in-vehicle technology in hybrid and ICE vehicles has led to declining sales and customer satisfaction.
KnightMotives wants to modernize the consumer experience across all vehicles within five years. Artificial Intelligence provides a unique opportunity to transform the in-vehicle experience, as well as the shopping, buying, and service/maintenance experience. Investing in this new technology will require a global shift in financial priorities.
KnightMotives also wants to enhance its unreliable online ordering system. Systems that let customers build their vehicle online for acquisition through a dealer are not providing the data or reliability dealers require, causing a strain in the relationship between KnightMotives and dealers. Service technicians and sales staff require improved tooling to advance dealer success, including for built-to-order vehicles.
Solution Concept —
KnightMotives wants to move from manufacturing cars to creating a complete and compelling “automotive experience.” The strategy emphasizes delivering a consistent experience across all models, developing AI-powered features, producing new revenue through data monetization, adopting a digital focus to distinguish its brand from competitors, and creating better tools for mechanics and salespeople.
Existing Technical Environment —
KnightMotives's IT is primarily on-premises, with some applications on major cloud platforms. Its supply chain operates on an outdated mainframe, and Enterprise Resource Planning (ERP) is also outdated, making new promotions and dealer discounts hard to implement. Dealers have no budget for new equipment. Vehicles are fragmented across multiple code bases, with significant technical debt caused by supporting backward compatibility. Network connectivity to manufacturing plants and vehicle connectivity in rural areas are challenges.
Business Requirements —
Key business requirements include cultivating a personalized relationship with the driver and providing a cohesive experience across all models. Establishing a better build-to-order model will reduce time on the lot and offer transparency to both dealers and customers. KnightMotives also seeks to monetize corporate data to fund new technology investments, because its current AI infrastructure is obsolete and corporate data is still siloed. Security is a primary concern because of previous data breaches. Compliance with European Union (EU) data-protection regulations, especially for emerging autonomous platforms, is critical.
KnightMotives plans significant investments in fully autonomous-driving capabilities, with initial implementation aimed at regions with favorable regulatory environments. Employee upskilling, attracting top-tier talent, and improving communication between business and technical teams are also critical goals.
Technical Requirements —
Modernizing the in-vehicle experience includes creating a consistent user experience (UX) that seamlessly integrates AI-powered features across all models, updating in-vehicle hardware and software in legacy models to support new UX features and AI capabilities, and ensuring dependable network connectivity—especially in rural areas—to support real-time AI features and data transmission.
Network upgrades are required to support greater data traffic and improve connectivity between plants and headquarters.
IT infrastructure modernization requires adopting a hybrid-cloud strategy to gain the benefits of both on-premises and cloud infrastructure, and progressively modernizing or replacing legacy systems to increase efficiency and agility.
Autonomous-vehicle development and testing requires investing in leading AI and machine-learning technologies, building a robust simulation environment, and ensuring compliance with evolving autonomous-vehicle regulations.
Data monetization and insights requires implementing a robust data-management platform, strict data-security and privacy measures, and scalable AI/ML infrastructure.
Increased emphasis on security and risk management involves implementing a comprehensive security framework to protect against cyber threats and data breaches, developing an incident-response plan, and providing security-awareness training to employees.
Delivering a delightful experience for dealers and customers requires improving the online build-to-order system; developing modern dealer tools to streamline dealer operations, including sales, service, and inventory management; and implementing a comprehensive Customer Relationship Management (CRM) system to track customer interactions, personalize experiences, and improve customer satisfaction.
Executive Statement —
KnightMotives is committed to improving safety and saving lives by using an extensive body of data—including driving, road conditions, behavioral studies, and crash-safety statistics—to create compelling digital experiences for drivers. Our AI consistently exceeds national safety statistics, ensuring that the distinctive and coveted KnightMotives experience is aligned across all vehicle models.
Michael Knight, KnightMotives CEO
For this question, use the KnightMotives Automotive case study. You are responsible for designing the network infrastructure architecture for KnightMotives's new Google Cloud environment. You need to design the new VPC topology. You want to provide guaranteed bandwidth and low latency between the plants and Google Cloud resources. What should you do?
ACreate a Standard Tier VPC. and ensure a subnet is available in the region closest to a plant. Establish a Cloud Interconnect between each subnet and the local plant.
BCreate a Standard Tier VPC. and ensure a subnet is available in the region closest to a plant Establish Direct Peering between Google's Edge Network and the local plant.
CCreate a Premium Ter VPand ensure a subnet is available in the region closest to a plant. Establish a Cloud Interconnect between each subnet and the local plant.
DCreate a Premium Ter VPC. and ensure a subnet is available in the region closest to a plant. Establish Direct Peering between Google's Edge Network and the local plant.
You developed an application with Cloud ML Engine that identifies famous paintings from uploaded images. You need to test the application and let specific people upload images during the next 24 hours. Not every user has a Google Account. How should users upload images?
AHave users upload the images to Cloud Storage. Protect the bucket with a password that expires after 24 hours.
BHave users upload the images to Cloud Storage using a signed URL that expires after 24 hours.
CCreate an App Engine web application where users can upload images. Configure App Engine to disable the application after 24 hours. Authenticate users via Cloud Identity.
DCreate an App Engine web application where users can upload images for the next 24 hours. Authenticate users via Cloud Identity.
The operations team at your company wants to retain Cloud VPN log events for one year. You need to configure the cloud infrastructure to store these logs. What should you do?
ASet up a filter in Cloud Logging and a Cloud Storage bucket as an export target for the logs you want to save.
BEnable the Compute Engine API, and then enable logging on the firewall rules that match the traffic you want to save.
CSet up a Cloud Logging Dashboard titled Cloud VPN Logs, and then add a chart that queries for the VPN metrics over a one-year time period.
DSet up a filter in Cloud Logging and a topic in Pub/Sub to publish the logs.
For this question, refer to the EHR Healthcare case study. EHR Healthcare recently suffered unauthorized access to its Kubernetes-based applications because credentials were compromised, resulting in a security breach. You must secure access between its on-premises systems and the Google Kubernetes Engine (GKE) environment, while preserving regulatory compliance and integrating with EHR's existing identity provider. What should you do?
ADeploy an OpenID Connect (OIDC) identity provider in GKE, and integrate it with Active Directory for centralized authentication and fine-grained authorization controls.
BEstablish a secure VPN tunnel between the on-premises network and the GKE VPC, and implement strict firewall rules to restrict access based on source IP addresses and ports.
CConfigure Workload Identity Federation with Active Directory to enable authentication and authorization for applications across both Kubernetes environments.
DCreate a service account with minimal permissions for each application running in GKE, and distribute the credentials securely to the on-premises systems.
You need to deploy Microsoft SQL Server on GCP. Management requires no downtime if a data center outage occurs in any zone within a GCP region. What should you do?
AConfigure a Cloud SQL instance with high availability enabled.
BConfigure a Cloud Spanner instance with a regional instance configuration.
CSet up SQL Server on Compute Engine, using Always On Availability Groups using Windows Failover Clustering. Place nodes in different subnets.
DSet up SQL Server Always On Availability Groups using Windows Failover Clustering. Place nodes in different zones.
Your company uses the Firewall Insights feature in Google Network Intelligence Center and has several firewall rules applied to Compute Engine instances.
You need to assess the efficiency of the applied firewall ruleset. When you open the Firewall Insights page in the Google Cloud Console, you find that no log rows are displayed. What should you do to troubleshoot this issue?
AEnable Virtual Private Cloud (VPC) flow logging.
BEnable Firewall Rules Logging for the firewall rules you want to monitor.
CVerify that your user account is assigned the compute.networkAdmin Identity and Access Management (IAM) role.
DInstall the Google Cloud SDK, and verify that there are no Firewall logs in the command line output.
You want to automate creation of a managed instance group. The VMs have many OS package dependencies, and you want to minimize startup time for new VMs in the instance group.
What should you do?
AUse Terraform to create the managed instance group and a startup script to install the OS package dependencies.
BCreate a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.
CUse Puppet to create the managed instance group and install the OS package dependencies.
DUse Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.
For this question, use the Mountkirk Games case study. You are responsible for the architecture of the new Game Backend Platform. The game communicates with the backend through a REST API.
You want to follow Google-recommended practices. How should you design the backend?
ACreate an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.
BCreate an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.
CCreate an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.
DCreate an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.
The database administration team has asked for your help improving the performance of its new database server running on Google Compute Engine. The database imports and normalizes its performance statistics and is built with MySQL running on Debian Linux. They have an n1-standard-8 virtual machine with 80 GB of SSD persistent disk.
What should they change to obtain better performance from this system?
AIncrease the virtual machine's memory to 64 GB
BCreate a new virtual machine running PostgreSQL
CDynamically resize the SSD persistent disk to 500 GB
DMigrate their performance metrics warehouse to BigQuery
EModify all of their batch jobs to use bulk inserts into the database
The marketing department plans to send a promotional email campaign. The development team wants to minimize direct operations management. They anticipate customer response ranging from 100 to 500,000 click-throughs per day. The link directs users to a simple website that explains the promotion and gathers user information and preferences.
Which infrastructure should you recommend?
AUse Google App Engine to serve the website and Google Cloud Datastore to store user data.
BUse a Google Container Engine cluster to serve the website and store data to persistent disk.
CUse a managed instance group to serve the website and Google Cloud Bigtable to store user data.
DUse a single Compute Engine virtual machine (VM) to host a web server, backend by Google Cloud SQL.
Dress4Win has asked you to recommend the machine types on which they should deploy their application servers.
How should you proceed?
APerform a mapping of the on-premises physical hardware cores and RAM to the nearest machine types in the cloud.
BRecommend that Dress4Win deploy application servers to machine types that offer the highest RAM to CPU ratio available.
CRecommend that Dress4Win deploy into production with the smallest instances available, monitor them over time, and scale the machine type up until the desired performance is reached.
DIdentify the number of virtual cores and RAM associated with the application server virtual machines align them to a custom machine type in the cloud, monitor performance, and scale the machine types up until the desired performance is reached.
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