Free Google Cloud Architect Actual Exam Questions - Question 9 Discussion
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 Their supply chain runs on an outdated mainframe, and Enterprise
Resource Planning (ERP) is also outdated, making new promotions and dealer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.
B/D? D risks write hotspots by starting with timestamp; B’s reversed timestamp key should distribute writes better and fit low-latency needs for telemetry data. B feels more aligned with time-series best practices.
B imo, because Cloud Bigtable is built for low-latency, high-throughput time-series data. Reversing the timestamp in the row key helps with efficient recent data queries, unlike D which risks hotspotting by starting with timestamp.
Option C seems right because using instance groups across regions in one project simplifies management and supports better scaling and failover without the hassle of multiple projects.
C, because instance groups provide better scalability and regional failover support.
Probably C here. Instance groups in different regions make failover smoother, and keeping everything in one project avoids extra management hassle. D’s project split feels unnecessary.
Maybe C makes the most sense because instance groups can auto-scale, and using one project keeps things simpler. D seems off since splitting projects adds unnecessary complexity and overhead.
A. Single instances don’t scale well or handle load balancing as effectively as instance groups. Using just two instances limits availability and might cause issues under traffic spikes or maintenance.
Probably C here as well. Using instance groups in different regions allows for better scalability and easier management. Plus, having them in the same project keeps networking and permissions simpler, which is key for smooth failover. D overcomplicates things by splitting across projects, and A doesn’t use instance groups, so it’s less efficient. B’s approach with on-premises failover seems off for cloud-native disaster recovery.
Makes sense to go with C since instance groups give you better scaling and management compared to single instances. Plus, keeping both groups in the same project simplifies permissions and network setup, so failover is easier to manage. A doesn’t scale well enough, B brings in complexity with on-premises failover, and D’s multiple projects seem unnecessary here. C just fits the disaster recovery needs best.
C imo, since using instance groups in different regions with HTTP load balancing handles failover smoothly. Options A and B don’t cover multiple instances/groups well, and D overcomplicates with separate projects.