Free Google Cloud Architect Actual Exam Questions
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disk theft and comply with FIPS 140-2 Level 3, the key must be stored in a dedicated
hardware security module (HSM). Which GCP service should be used?
It’s A because only Cloud HSM provides dedicated hardware meeting FIPS 140-2 Level 3.
D imo, because Cloud KMS software-backed keys don’t provide the hardware isolation needed for FIPS 140-2 Level 3, which is a strict requirement here. Cloud Storage with CMEK (C) just manages encryption keys but doesn’t guarantee hardware-level security. IAM (B) is just about permissions, not actual key storage. So A makes sense since it uses dedicated hardware modules specifically designed for high-assurance key protection.
perform complex aggregations and windowing operations on this data with
millisecond latency, and the resulting insights must be streamed to an operational
dashboard. Which service is the most suitable for the stream processing
component?
C - Dataflow handles real-time windowing and aggregations way better than others.
Maybe C here, since Dataflow is designed for real-time stream processing with low latency and supports complex windowing and aggregations better than the others.
application using the `gcloud` CLI, but you want to test it before routing all traffic to
it. Which command is appropriate?
D imo, because --no-promote avoids routing traffic right away.
D/C? D looks right because --no-promote lets you deploy without shifting traffic immediately. C doesn’t even seem like a valid command for App Engine, so it’s probably out.
a private SaaS application like Salesforce. Which Vertex AI feature allows the model
to interact with external APIs securely?
D. Vertex AI Extensions is built for enabling secure interaction with external APIs, which fits the need here. Options A and B are more about data storage and pre-built models, not API security. C focuses on data loss prevention but doesn't enable the model to make API calls directly—it’s more of a protection tool. Since the question is about securely connecting the generative AI to Salesforce’s APIs, D is the only one that really matches that use case.
C/D? D definitely fits since it’s about secure API calls, but I’m wondering if Cloud Data Loss Prevention (C) plays a role in protecting sensitive info while interacting with Salesforce. Might be worth considering both.
application requires high-availability and regional fault tolerance. The front-end is
stateless and the backend is a PostgreSQL database. They want to minimize latency
for customers across two US regions. Which set of GCP services should you
recommend for a highly available, multi-region deployment?
C. Cloud Spanner’s multi-region capabilities fit the high availability and low latency needs better than Cloud SQL in this scenario, plus global HTTP(S) load balancing handles traffic efficiently across regions.
It’s A because having two Partner Interconnects in different metro zones still provides strong redundancy without the complexity and cost of multi-metro setups like D. That fits better for typical production-level design.
Company Overview
Cymbal is an online retailer experiencing significant growth. The retailer specializes
in a large assortment of products spanning several retail sub-verticals, which
makes managing their extensive product catalog a constant challenge.
Solution Concept
Cymbal wants to modernize its operations and enhance the customer experience in
three core areas:
● Catalog and Content Enrichment: Cymbal wants to automate and improve the
accuracy of their product catalog by utilizing gen AI to generate product attributes,
descriptions, and images from supplier-provided information. This solution will
streamline their catalog management, reduce manual effort and errors, and ensure
information is consistent across all their sales channels.
● Conversational Commerce with Product Discovery: To enhance customer
engagement and drive sales conversion, Cymbal wants to implement a
Conversational Commerce solution. This solution will involve integrating AI-
powered virtual agents into their website and mobile app to provide customers with
a personalized and intuitive shopping experience through natural language
conversations. These agents will utilize Google Cloud's Discovery AI to process user
requests and retrieve the most relevant products based on each customer's needs
and preferences, creating a more engaging and satisfying shopping journey.
● Technical Stack Modernization: To streamline operations and reduce costs
around manual processes, data transfer, error handling and remediation, Cymbal
wants to modernize their technical stack with cloud-based infrastructure, secure
and efficient data handling, 3rd party integrations, and proactive monitoring and
security.
Existing Technical Environment
Cymbal currently relies on the following environment: ● A mix of on-premises and
cloud-based systems. ● A variety of databases, including MySQL, Microsoft SQL
Server, Redis, and MongoDB, to store and manage its vast product catalog and
customer data. ● Kubernetes clusters to run containerized applications. ● Legacy
file-based integrations with on-premises systems, including SFTP file transfers, ETL
batch processing. ● A custom-built web application which allows customers to
browse the product catalog by querying the relational databases for names and
categories of products. ● An IVR (Interactive Voice Response) system to handle
initial customer calls and route them to the appropriate departments or agents. ●
Call center agents who receive transferred calls from the IVR system and manually
enter orders into the system when a customer can’t complete a transaction on
their own. ● Various open source tools for monitoring such as Grafana, Nagios,
and Elastic. The current technical environment has encountered significant
challenges: manual processes are time-consuming and error-prone, data silos limit
a unified view of the customer journey, and integrating new technologies is difficult.
Business Requirements
Cymbal has outlined these key business requirements for the gen AI solution: ●
Automate Product Catalog Enrichment: Reduce manual effort, minimize errors, and
ensure accuracy and consistency across the product catalog. ● Improve Product
Discoverability: Enhance search relevance and enable customers to find products
more efficiently. ● Increase Customer Engagement: Create a more interactive and
personalized shopping experience to improve customer satisfaction and potentially
reduce product returns. ● Drive Sales Conversion: Provide a more intuitive and
helpful shopping experience to improve sales conversion rates and drive revenue
growth. ● Reduce costs: Reduce call center staffing costs and data-center hosting
costs.
Technical Requirements
● Attribute Generation: Accurately derive relevant product attributes from various
supplier data, including titles, descriptions, and images, ensuring the attributes
align with the product category and Cymbal's existing catalog structure. ● Image
Generation and Enhancement: Generate different product image variations from a
base image (e.g., showcasing various colors). It should also support background
changes, product color adjustments, and the addition of text overlays. ● Automate
Product Discovery: Process customer requests expressed in natural language and
return highly relevant product results. ● Scalability and Performance: The solution
must handle Cymbal's extensive product catalog and accommodate their
anticipated growth without compromising performance or user experience. ●
Human-in-the-Loop (HITL) Review: Provide a user interface (UI) for associates to
review and manage gen AI-generated content, allowing them to approve, reject, or
modify suggestions before updating the product catalog. ● Data Security and
Compliance: Ensure all customer data, including product information and
interactions with virtual agents, are handled securely and comply with relevant
industry regulations.
Executive Statement
By implementing Google Cloud's Generative AI for Digital Commerce solutions,
Cymbal can transform its online retail operations to improve efficiency, enhance
customer experience, and drive revenue growth. Key benefits for Cymbal include:
● Reduced operational costs through automation of catalog management tasks. ●
Increased efficiency and speed in onboarding new products and updating existing
ones. ● Improved accuracy and consistency of product information across all
sales channels. ● A more engaging and personalized shopping experience that
caters to modern customer preferences for conversational commerce. ●
Enhanced product discoverability leading to higher conversion rates and increased
sales.
This strategic investment in generative AI will position Cymbal to remain
competitive and thrive in the rapidly evolving landscape of online retail.
------------------------------------------------
Query
Cymbal Retail currently runs some legacy inventory applications on-premises in
their private data centers and some in Google Kubernetes Engine (GKE). They want
to modernize their EKS (Amazon Elastic Kubernetes Service) clusters to ensure
consistent policy management and security across all environments.
Which solution is most appropriate?
A) Migrate all EKS workloads to GKE Standard to eliminate multi-cloud
overhead.
B) Use Anthos to manage GKE, on-premises clusters, and EKS clusters through
a single unified control plane.
C) Deploy Model Garden containers directly onto EKS to handle Al inference
locally.
D) Use Bigtable replication to sync data between EKS and GKE.
I’m with the crowd on B here. Anthos is really designed for exactly this kind of scenario — managing Kubernetes clusters across different environments while keeping policies and security consistent. A feels a bit extreme since migrating everything to GKE might not be feasible or necessary, especially if they want to keep some workloads on AWS. C and D don’t really address the core issue of cluster management and policy consistency. So B seems like the best fit for Cymbal’s modernization goals without forcing a full cloud migration.
B makes the most sense here since Anthos is built for managing hybrid and multi-cloud Kubernetes clusters with consistent policies and security. It directly addresses the need for unified control across EKS, GKE, and on-prem.
need full access to deploy and manage resources in their own specific `dev-`
projects. However, they must be prevented from creating or modifying VPC
Network and Firewall Rules in the shared Host project. What is the most precise
way to enforce this separation of duties?
D makes sense since giving Project Owner on dev-projects lets devs fully manage their own resources, while Compute Network User on the Host project restricts them from changing networks or firewalls. Seems more straightforward than relying on Org Policies.
Bigtable handles massive, real-time data streams way better than Stackdriver for GBs of KPIs. So A fits the low-latency, high-volume capture scenario best. Definitely not C or D for real-time stuff. A
requires an active-active setup across multiple regions (us-central1, europe-
west1). Data must be written to both regions concurrently with low latency and high
consistency. Which database should be chosen?
Probably A here because changing IPs to avoid overlap is the cleanest fix long term. NAT can get messy and firewalls or route blocking won't solve the core problem of overlapping CIDRs.
Maybe B makes the most sense here since NAT can translate those overlapping IPs without needing a full network redesign or blocking routes, which might cause connectivity issues.
Company Overview
KnightMotives is a car manufacturer specializing in autonomous, self-driving
vehicles, including Battery Electric Vehicles (BEVs), hybrids, and traditional internal
combustion engine (ICE) vehicles. While KnightMotives has made strides with the
in-vehicle experience in their BEV fleet, the hybrid and ICE vehicles have yet to
implement these new systems and are viewed poorly by critics and drivers. The lack
of modern in-vehicle technology in hybrid and ICE vehicles has resulted in declining
sales and customer satisfaction. KnightMotives wants to modernize the consumer
experience across all vehicles within five years. Artificial Intelligence offers a unique
opportunity to revolutionize the in-vehicle experience, as well as the shopping,
buying, and service/maintenance experience. Investment in this new technology
will require a shift in financial priorities on a global scale. KnightMotives also wants
to improve their online ordering system, which is unreliable. Systems for customers
to build their vehicle online for acquisition through a dealer are not delivering the
data or reliability that dealers need, causing a strain in the relationship between
KnightMotives and dealers. Service technicians and sales staff need better tooling
to enhance dealer successes, including built-to-order vehicles.
Solution Concept
KnightMotives wants to shift from manufacturing cars to creating a complete and
compelling "automotive experience." Their strategy prioritizes delivering a
consistent experience across all models, developing AI-powered features,
generating new revenue from data monetization, adopting a digital focus to
differentiate their brand from competitors, and developing better tools for
mechanics and salespeople.
Existing Technical Environment
KnightMotives's IT is largely on-premises with some applications on major cloud
platforms. Their supply chain runs on an outdated mainframe, and Enterprise
Resource Planning (ERP) is also outdated, making new promotions and dealer
discounts difficult to implement. Dealers have no budget for new equipment. There
is fragmentation across vehicles with multiple code bases, and significant technical
debt from supporting backwards compatibility. Network connectivity to
manufacturing plants and vehicle connectivity in rural areas are challenges.
Business Requirements
Key business requirements include fostering a personalized relationship with the
driver and delivering a cohesive experience across all models. Creating a better
build-to-order model will reduce time on the lot and provide transparency for both
dealers and customers. Additionally, KnightMotives seeks to monetize corporate
data to finance new technology investments, as their current AI infrastructure is
obsolete and corporate data remains siloed. Security is a paramount concern due
to past data breaches. Adherence to European Union (EU) data protection
regulations, especially for emerging autonomous platforms, is critical.
KnightMotives plans to make significant investments in fully autonomous driving
capabilities, with initial implementation targeting regions with favorable regulatory
environments. Prioritizing employee upskilling, attracting top-tier talent, and
fostering better communication between business and technical teams are also
critical objectives.
Technical Requirements
● Modernizing the in-vehicle experience includes developing 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 reliable network connectivity, especially
in rural areas, to support real-time AI features and data transmission. ● Network
upgrades are necessary to support increased data traffic and improve connectivity
between plants and headquarters. ● IT infrastructure modernization requires
adopting a hybrid cloud strategy to leverage the benefits of both on-premises and
cloud infrastructure, and gradually modernizing or replacing legacy systems to
improve efficiency and agility.
Autonomous vehicle development and testing requires investing in cutting-edge AI
and machine learning technologies, building a robust simulation environment, and
ensuring compliance with evolving regulations related to autonomous vehicles. ●
Data monetization and insights requires implementing a robust data management
platform, strict data security and privacy measures, and a scalable AI/ML
infrastructure. ● Increased focus 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. ● Providing 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 enhancing safety and saving lives by leveraging an
extensive body of data—encompassing driving, road conditions, behavioral studies,
and crash safety statistics—to create compelling digital experiences for drivers. Our
AI consistently outperforms national safety statistics, ensuring the unique and
coveted KnightMotives experience is aligned across all our vehicle models. Michael
Knight, KnightMotives CEO
----------------------------------------------------------
Query
KnightMotives aims to capture telemetry data from millions of vehicles to provide
proactive maintenance alerts. Given their requirement for low-latency writes and
the need to handle high-velocity time-series data from vehicle sensors, which
storage and processing architecture best aligns with Google Cloud best practices
for this scenario?
B/D? B’s reversed timestamp avoids hotspots; D’s chronological writes seem riskier for scaling.
B, reversed timestamp avoids hotspots and supports quick lookups by vehicle ID.
remain private (no external IP address) but needs to download security patches
from the internet. What is the minimum networking component required to enable
this outbound traffic?
Since the VM has no external IP, it can’t directly access the internet. That rules out options without NAT. So, would enabling Private Google Access (C) alone let it reach external patch servers? Seems unlikely.
It’s B for me. The Horizontal Pod Autoscaler will handle scaling the pods based on load, and cluster autoscaling keeps the nodes in check. Using a Service of type LoadBalancer simplifies HTTPS traffic because GKE provisions a cloud load balancer with SSL termination out of the box, so you don’t have to set up Ingress and worry about extra components. A is good but depends on having an Ingress controller properly configured, which might not be guaranteed. B is straightforward and covers autoscaling plus SSL load balancing more directly.
cluster as part of their CI/CD pipeline. Which GCP service is the most suitable for
executing the automation and managing the deployment process?
D, since it focuses on managing deployments specifically, not just builds.
Guessing C since Cloud Build handles CI/CD automation and deploys Helm charts well.
protected with rate limiting, quota enforcement, and authentication for partners.
Which GCP service is recommended to sit in front of the Cloud Run service?
Option C definitely makes the most sense here since Cloud Endpoints and Apigee X are designed to manage APIs with built-in support for authentication and rate limiting. A load balancer (A) can distribute traffic but doesn’t enforce quotas or auth natively. VPC Service Controls (B) focus more on securing data boundaries, not API usage controls. Cloud Functions (D) is just a compute option and doesn’t provide an API management layer. So, for protecting the API with those specific features, C is the way to go.
Makes sense to rule out A and D since they don’t handle authentication or rate limits directly. B is more about security perimeters, so C fits best for API controls like quota and auth. C it is.
constraints. The operations team needs to be immediately notified when any
container in the production cluster experiences an Out-of-Memory (OOM) error or
high CPU utilization. Which set of tools should be configured for effective
monitoring and alerting?
Makes sense that D is the go-to for alerting on OOM and CPU, easy to automate. D
D, since Cloud Monitoring alerts can be set up directly from logs for OOM and CPU spikes.
system that multiple Compute Engine VMs need to mount and access concurrently
for configuration files and temporary storage. What is the most appropriate,
managed GCP service?
D Cloud Filestore is the only managed service here that provides a true shared file system with POSIX compliance, which is essential for legacy apps expecting standard file semantics. The other options are more like object or database stores and won’t support mounting as a regular file system by multiple VMs concurrently. So this really rules out A, B, and C for this case.
D Cloud Filestore is designed for exactly this use case: shared, POSIX-compliant file systems accessible by multiple VMs at once. The others don’t support traditional file system semantics needed here.
A large enterprise is migrating all its production workloads to Google Cloud. The security team insists that all outbound internet traffic from the VPC network be inspected by their proprietary, on-premises Intrusion Detection System (IDS) before leaving the Google network. What networking feature must be implemented?
Option A seems off since it only mentions a firewall VM inside Google Cloud, not routing through on-prem IDS. D just manages NAT, so it won't force traffic through external inspection. Could B still be the only real choice?
B imo, it’s the only one that explicitly routes traffic through on-prem IDS via VPN or Interconnect.