Google Professional Cloud Architect Exam Dumps & Practice Test Questions
Question 1:
TerramEarth operates a global fleet of heavy machinery across mining and agriculture sectors. Of the 20 million deployed vehicles, only 200,000 are currently connected via cellular for near real-time telemetry. The rest rely on local storage and data uploads through FTP during maintenance visits, causing a 3-week delay in generating performance reports. This results in replacement part delays and vehicle downtimes lasting up to 4 weeks.
Management wants to:
Cut downtime to under 1 week
Enable real-time data services for dealers
Support data-driven agricultural partnerships
Modernize their technology architecture
What solution would best help TerramEarth minimize latency and meet these strategic goals?
A. Switch to binary data formats, upgrade to SFTP, and integrate machine learning analytics
B. Replace FTP with streaming transport, adopt binary formats, and use ML analytics
C. Expand fleet-wide cellular connectivity, move to streaming transport, and apply ML analytics
D. Upgrade to SFTP, use ML analytics, and boost dealer inventory availability
Correct Answer: C
Explanation:
The crux of TerramEarth’s challenge lies in the latency introduced by FTP-based batch uploads, which delay data ingestion and report generation. Their current system depends on manual data uploads, creating a 3-week lag that hinders proactive maintenance and parts replacement.
To modernize this, a three-pronged approach is most effective:
Expand Real-Time Connectivity:
Only 1% of the fleet (200,000 out of 20 million) is currently connected. Scaling this to 80% ensures that a majority of vehicles can transmit data live, enabling continuous monitoring and predictive maintenance.Adopt Streaming Transport:
Shifting from FTP to a real-time streaming solution like Azure Event Hubs or Apache Kafka allows instant ingestion of data, significantly reducing delay and supporting near real-time analytics.Use Binary Data Formats:
Transitioning from text-based CSV to efficient binary formats (e.g., Avro, Protobuf) enhances transmission speed and parsing efficiency, reducing payload size and resource load during processing.
Additionally, machine learning models can use this streaming data to predict part failures and detect anomalies, reducing unplanned downtime and aligning with the business’s goal of proactive support.
While other options (like switching to SFTP or increasing dealer inventory) may provide minor incremental gains, only Option C addresses the root problem—real-time data unavailability—while also setting up a scalable, agile analytics platform for future growth and external partnerships.
Question No 2:
TerramEarth is undergoing a digital transformation to shift from on-premises infrastructure to Google Cloud. Their existing architecture consists of data uploads via FTP, gzip-compressed CSV files, and a traditional ETL pipeline feeding into a warehouse. This process introduces a 3-week reporting delay. Although predictive maintenance has reduced downtime by 60%, customers still experience delays of up to 4 weeks.
With cloud adoption, which internal enterprise processes are expected to change most significantly?
A. IT budget allocation, LAN infrastructure, and capacity planning
B. Planning for capacity, cost modeling, and budget classification
C. Resource planning, system utilization tracking, and data center upgrades
D. Data center expansion, cost modeling (TCO), and performance utilization measurement
Correct Answer: D
Explanation:
As TerramEarth migrates from legacy on-prem infrastructure to Google Cloud Platform (GCP), the most disruptive changes will occur in infrastructure management, cost forecasting, and system monitoring.
Data Center Expansion:
Traditional capacity growth meant investing in physical servers and expanding data centers. Cloud-native services like BigQuery and Cloud Storage eliminate the need for physical provisioning, enabling elastic scaling without upfront hardware costs.TCO (Total Cost of Ownership) Calculations:
On-premise models involve CapEx-heavy investments—buying hardware, maintaining space, and staffing IT operations. Cloud adoption shifts this to OpEx-based, consumption-driven billing. As a result, TerramEarth must overhaul its financial planning processes to align with GCP’s pay-as-you-go model.Utilization Measurement:
Previously, TerramEarth needed to track server utilization to plan when to scale up infrastructure. In the cloud, autoscaling and serverless technologies like Dataflow and Pub/Sub make resource provisioning dynamic, reducing reliance on manual utilization metrics.
In contrast:
A and B include LAN and budget reallocation, which may change but aren’t primary disruptors.
C mentions data center upgrades, which are obsolete under cloud models.
By selecting Option D, we capture the core enterprise transformations required for a successful move to GCP—those that touch on infrastructure elimination, cost model restructuring, and automated performance handling.
Question No 3:
TerramEarth, a major global manufacturer of heavy-duty machinery for mining and agriculture, operates over 20 million vehicles globally. Each machine emits 120 telemetry fields per second for monitoring and predictive maintenance. Currently, only 200,000 vehicles transmit this data via cellular networks; the rest store it locally and upload it through FTP during service visits.
Their legacy infrastructure compresses CSV files using gzip on Linux systems, uploads them via FTP, processes them through an ETL pipeline, and stores them in a data warehouse. However, this creates a 3-week delay in reporting, negatively affecting vehicle downtime and inventory planning.The organization aims to reduce unplanned downtimes to under a week, provide better insights to equipment dealers, and form partnerships in agriculture by using telemetry data more effectively.
To meet these goals, which solution would best increase data reliability and reduce the time taken to transfer vehicle data to the ETL system?
A. Use a single Google Kubernetes Engine (GKE) cluster of FTP servers and store data in a Multi-Regional bucket
B. Use multiple GKE clusters with FTP servers in various regions, saving data to regional Multi-Regional buckets
C. Transfer data directly to Multi-Regional Cloud Storage buckets using Google APIs over HTTP(S)
D. Transfer data directly to Regional Cloud Storage buckets using Google APIs over HTTP(S)
Correct Answer: C
Explanation:
The most effective solution for improving both reliability and data transfer speed is to bypass the outdated FTP system entirely and directly upload vehicle data to Google Cloud Multi-Regional Storage using HTTP(S)-based Google APIs, as suggested in Option C.
Unlike FTP, which restarts file uploads from the beginning after a failure, Google Cloud’s HTTP(S) uploads support resumable transfers. This is especially important when dealing with intermittent cellular connections in remote locations. Moreover, Multi-Regional buckets automatically replicate the data across multiple regions within a continent, ensuring high availability and low-latency access for downstream ETL processes.
This approach removes the three-week lag caused by FTP and centralized file handling. It significantly accelerates the pipeline from data generation to processing, which is vital for predictive maintenance and minimizing vehicle downtime. Data becomes readily available to dealers and partners, supporting strategic initiatives like inventory optimization and agricultural collaborations.
Additionally, using Google Cloud Storage and APIs aligns with cloud-native architecture principles, allowing TerramEarth to scale globally, enforce robust security policies, and integrate with modern analytics tools and machine learning pipelines.
While Option D also uses HTTP(S) APIs, it relies on Regional buckets that don’t offer the redundancy or latency improvements provided by Multi-Regional storage. Options A and B, meanwhile, retain FTP as the core transport mechanism—reintroducing the very reliability issues TerramEarth seeks to avoid.
Thus, Option C offers the most reliable, modern, and scalable solution for their data ingestion needs.
Question No 4:
TerramEarth operates 20 million mining and farming vehicles globally. Around 200,000 of these vehicles transmit a total of 9 TB of telemetry data daily via cellular networks, with data stored in Google Cloud Storage (GCS) across regional buckets in the US, Europe, and Asia.
The CTO wants to investigate engine failure patterns occurring after 100,000 miles. You’ve been asked to recommend a cost-effective way to process this large dataset using Google Cloud’s services.
Which strategy is the most cost-efficient for running the required data analysis?
A. Transfer all telemetry data to a single zone and analyze it using a centralized Dataproc cluster
B. Move all data to one region and process it with a regional Dataproc cluster
C. Use regional Dataproc clusters to preprocess and compress the data, then move it to a Multi-Regional bucket for analysis
D. Preprocess and compress the data in each region using Dataproc clusters, then move the results to a single Regional bucket for final processing
Correct Answer: D
Explanation:
The most cost-effective and operationally efficient strategy is described in Option D: preprocessing and compressing telemetry data locally within each region, followed by moving the reduced dataset to a centralized Regional bucket for final processing.
This approach adheres to a “process data where it resides” strategy, which minimizes inter-region egress charges—a key cost consideration when handling massive, distributed datasets. By running Cloud Dataproc clusters in each region (US, Europe, and Asia), the system preprocesses and compresses the raw telemetry data close to the source. This reduces the size of the data before any cross-region transfer occurs.
Once compressed, the now much smaller dataset can be transferred to a single regional bucket for final aggregation and analysis. Since less data is moved across regions, network costs and latency are significantly reduced.
In contrast, Option A and B involve moving all raw data to a centralized zone or region upfront, triggering massive data transfer costs and potentially creating bandwidth bottlenecks. Option C, although similar to D in its preprocessing step, suggests using a Multi-Regional bucket, which is typically more expensive and may introduce data consistency issues that are unnecessary for this use case.
By localizing heavy computation and only centralizing lightweight outputs, Option D leverages Google Cloud's strengths in distributed processing and regional storage efficiency. It ensures that TerramEarth gains timely insights into vehicle breakdowns without incurring excessive cloud storage or transfer expenses.
This distributed processing model also allows the company to scale as more vehicles come online, aligning with long-term growth goals. Hence, Option D delivers on cost, performance, and scalability for TerramEarth’s analytics objectives.
Question No 5:
TerramEarth, a global leader in manufacturing heavy equipment for the mining and agriculture sectors, operates a fleet of 20 million smart vehicles that generate massive amounts of telemetry data, totaling 9 TB per day. While most vehicles store data locally and upload it during maintenance, around 200,000 vehicles are connected to cellular networks and stream data in real time. The company is exploring machine learning models to improve operations and reduce costs, while also seeking to optimize its storage solution for the large volume of data.
What action should TerramEarth take to prepare for training machine learning models and efficiently store the telemetry data?
A. Store compressed hourly snapshots in GCS Nearline
B. Stream and compress data in Dataflow, store in BigQuery
C. Stream and compress data in Dataflow, store in Cloud Bigtable
D. Store compressed hourly snapshots in GCS Coldline
Correct Answer: C. Stream and compress data in Dataflow, store in Cloud Bigtable
Explanation:
TerramEarth’s goal is to use its vast stream of telemetry data (9 TB per day) for training machine learning models while keeping storage costs low. To meet these objectives, it is crucial to have a scalable, real-time data ingestion and storage solution that can support high throughput and efficient querying for machine learning (ML) training.
The optimal solution in this case is Cloud Bigtable. This service is well-suited for time-series data, such as telemetry from vehicles, where large volumes of data are generated rapidly and require real-time querying for analysis. Cloud Bigtable provides low-latency access to the most recent data, which is ideal for real-time analytics and machine learning workflows.
To further enhance this setup, Dataflow is used to stream and compress the telemetry data in real-time, which helps reduce storage costs and optimize data transmission. Dataflow processes the data as it streams, applying necessary transformations before it is stored in Bigtable. This combination allows TerramEarth to handle high-volume ingestion efficiently while ensuring that data is readily available for ML model training.
Let’s examine why the other options are not suitable:
Option A (GCS Nearline) and Option D (GCS Coldline): These storage classes are designed for infrequent access and long-term archiving, not for real-time data analytics or machine learning. Additionally, Google Cloud Storage (GCS) is not optimized for time-series data querying, which is crucial for TerramEarth’s use case.
Option B (BigQuery): While BigQuery is excellent for analytics, it is more suited for batch queries and may incur higher costs when processing high-ingest-rate workloads like those generated by 200,000 connected vehicles streaming data at a rapid pace.
In conclusion, using Dataflow to stream and compress telemetry data, and storing it in Cloud Bigtable, is the most cost-effective and scalable solution for TerramEarth’s needs. This setup enables real-time data processing and efficient querying, laying a solid foundation for training machine learning models.
Question No 6:
TerramEarth, a global leader in heavy equipment manufacturing, is moving toward autonomous vehicles for the agricultural sector. As the company focuses on integrating connected solutions, security is a major concern, particularly regarding communication between internal modules and the integrity of boot-time software. To ensure strong security during autonomous vehicle operations,
Which two architectural strategies should TerramEarth implement?
A. Treat every microservice call between modules on the vehicle as untrusted
B. Require IPv6 for connectivity to ensure a secure address space
C. Use a trusted platform module (TPM) and verify firmware and binaries on boot
D. Use a functional programming language to isolate code execution cycles
E. Use multiple connectivity subsystems for redundancy
F. Enclose the vehicle's drive electronics in a Faraday cage to isolate chips
Correct Answers:
A. Treat every microservice call between modules on the vehicle as untrusted
C. Use a trusted platform module (TPM) and verify firmware and binaries on boot
Explanation:
As TerramEarth transitions to autonomous vehicles, ensuring robust security in their architecture is essential. Autonomous vehicles rely heavily on embedded software and connectivity between modules, making them vulnerable to both internal and external cyber threats. Therefore, it is crucial to implement architectural strategies that ensure both the integrity of the software and secure communications between vehicle modules.
Option A (Treat every microservice call between modules on the vehicle as untrusted) is a core principle of zero-trust architecture. In a zero-trust model, no communication, even within the same system, is trusted by default. Every microservice call between vehicle modules should be verified, authenticated, and authorized to prevent any unauthorized access or movement of malicious code. This significantly reduces the potential attack surface and helps safeguard the vehicle from internal threats or compromised components.
Option C (Use a trusted platform module [TPM] and verify firmware and binaries on boot) is another essential strategy for securing autonomous vehicles. A TPM ensures that the vehicle’s boot process remains secure by validating the integrity of the firmware and binaries before execution. This prevents unauthorized modifications to the vehicle’s software, which could otherwise lead to vulnerabilities and compromise the vehicle’s operation from the moment it powers on. TPM provides hardware-based attestation, further strengthening the overall security architecture.
Now, let’s discuss why the other options are not as effective:
Option B (IPv6): While IPv6 offers a larger address space, it does not inherently improve security. The primary concern here is securing the communication between internal modules, not the addressing scheme.
Option D (Functional programming): While functional programming has advantages in certain scenarios, it does not specifically address the security needs for autonomous vehicle communication or firmware verification.
Option E (Multiple connectivity subsystems): Redundancy can improve reliability but does not directly address the security concerns related to internal module communication or software integrity.
Option F (Faraday cage): A Faraday cage can block signals, but it is impractical and insufficient for ensuring the security of connected systems and embedded software. It does not protect against cyber threats or software tampering.
In conclusion, the combination of zero-trust communication (A) and secure boot using TPM (C) offers a comprehensive security approach that addresses both the integrity of vehicle modules and the safety of the boot-time software, providing a strong foundation for TerramEarth’s autonomous vehicle architecture.
Question No 7:
TerramEarth, a global leader in manufacturing heavy equipment primarily for the mining (80%) and agricultural (20%) industries, operates a network of over 500 dealers and service centers across 100 countries. The company, founded in 1946, values innovation, customer commitment, and employee well-being.
TerramEarth’s fleet includes 20 million vehicles worldwide, each collecting 120 fields of sensor data per second. However, only around 200,000 vehicles are connected via cellular networks, generating approximately 9TB of data per day. Most of the vehicles store data locally, only accessible during in-person servicing.
The existing data pipeline uses legacy systems: Linux-based infrastructure that transfers gzipped CSV files over FTP to an on-premises data warehouse. This setup introduces a delay of up to 3 weeks before actionable insights can be gathered. Although this process has reduced vehicle downtime by 60%, some customers still face delays of up to 4 weeks for parts due to the outdated data.
To meet its business goals, including reducing downtime to under 1 week, providing dealers with timely usage insights, and expanding into agriculture, the company needs to improve its technical approach.
Your task is to determine the best solution to improve the operational efficiency of all 20 million vehicles, both connected and unconnected, by leveraging real-time and historical data to adjust operational parameters like oil pressure based on environmental factors.
Which solution best supports this objective?
A) Have engineers review the data for patterns and create rule-based algorithms for automatic adjustments.
B) Collect all operational data, train machine learning models to identify optimal operations, and run them locally for real-time adjustments.
C) Implement a Google Cloud Dataflow streaming job with a sliding window and use Google Cloud Messaging to push adjustments.
D) Collect all operational data, train ML models on Google Cloud ML Platform, and make remote operational adjustments based on predictions.
Correct Answer: B
Explanation:
The best solution for TerramEarth is Option B, which involves capturing all operating data, training machine learning (ML) models to identify optimal operations, and running them locally for real-time adjustments. This solution is the most effective for increasing efficiency across the entire fleet, including both connected and unconnected vehicles.
Since the majority of TerramEarth’s vehicles are not connected to cellular networks, relying on cloud-based solutions (as suggested in Options C and D) would not address the needs of the unconnected fleet. The focus on real-time, locally-run machine learning models allows for immediate, in-vehicle adjustments based on both historical and real-time data. These models can analyze environmental factors such as terrain, temperature, and altitude, as well as operational parameters like oil pressure, to optimize vehicle performance without relying on continuous cloud connectivity.
Option A, while offering a straightforward approach, is limited to rule-based algorithms, which lack the flexibility and adaptability of machine learning models. Rule-based systems can be static and fail to account for complex, dynamic conditions that change in real-time, which reduces their scalability and effectiveness compared to ML models.
Option C suggests a streaming architecture that works for connected vehicles but does not address the unconnected ones, leaving a significant portion of the fleet without optimization. This also introduces additional complexity and dependency on cloud connectivity, which may not be ideal for all vehicles.
Option D also proposes using cloud-based ML models, but like Option C, it focuses only on connected vehicles, limiting the solution’s effectiveness across the entire fleet. Moreover, remote adjustments based on predictions would create a reliance on connectivity and introduce latency, which could hinder real-time optimization.
By implementing locally run ML models, TerramEarth ensures that all vehicles benefit from real-time adjustments, optimizing fuel efficiency, reducing wear and tear, and minimizing downtime—core objectives for the business. Additionally, this approach enables future innovation, such as over-the-air updates for connected vehicles and syncing insights for unconnected vehicles during routine maintenance, further improving overall operational efficiency.
Question No 8:
You are tasked with designing a solution for an e-commerce company that needs to handle traffic spikes during peak seasons. The application is built on Google Cloud, and the company wants to ensure high availability and scalability while keeping costs under control.
Which Google Cloud service would you recommend for automatically scaling the application based on traffic patterns?
A. Google Cloud Functions
B. Google Kubernetes Engine (GKE)
C. Google Cloud Run
D. Google Compute Engine with Managed Instance Groups
Correct Answer: C
Explanation:
Google Cloud Run is the most appropriate service for handling automatic scaling based on traffic patterns. It is a serverless compute service that automatically scales applications up or down based on incoming requests. This is particularly useful for handling traffic spikes during peak seasons, as the service can scale quickly and efficiently.
Here’s why Option C is correct:
Serverless nature: Google Cloud Run allows you to run containerized applications without managing the underlying infrastructure. It automatically adjusts the number of instances running based on the load, ensuring that the application can handle increased traffic without requiring manual intervention.
Cost-effective: Since Cloud Run scales dynamically based on demand, you only pay for the compute time your application actually uses, making it highly cost-efficient during periods of low traffic.
Scalability: It is designed for scenarios where applications need to handle varying levels of traffic and automatically scale to meet demand, which is a key requirement for the e-commerce company.
Let’s analyze the other options:
Option A (Google Cloud Functions): While Google Cloud Functions is also serverless, it is better suited for event-driven workloads and functions that need to execute in response to specific triggers. It’s less ideal for running an entire e-commerce application, which would require more flexibility and control.
Option B (Google Kubernetes Engine): GKE is a great option for containerized workloads that require more control over infrastructure, but it does not automatically scale the application based on traffic in a completely serverless manner. Kubernetes clusters can scale, but they require more configuration and management than Cloud Run.
Option D (Google Compute Engine with Managed Instance Groups): This service can also scale but requires manual configuration of scaling policies and virtual machine instances. It is more suited for applications where you need more control over the underlying infrastructure.
Thus, Option C: Google Cloud Run is the most efficient and cost-effective solution for an application that needs to automatically scale based on varying traffic patterns.
Question No 9:
You are designing a solution for a global company that needs to store and analyze large amounts of sensitive data in Google Cloud. The data must be encrypted both in transit and at rest, and only authorized users should have access to specific datasets.
Which Google Cloud service should you use to ensure compliance with security and data privacy requirements?
A. Google Cloud Storage with Customer-Managed Encryption Keys (CMEK)
B. Google BigQuery with automatic encryption enabled
C. Google Cloud Spanner with encryption at rest
D. Google Cloud SQL with SSL/TLS encryption
Correct Answer: A
Explanation:
To ensure that the company’s sensitive data is encrypted both in transit and at rest, and that only authorized users can access specific datasets, Google Cloud Storage with Customer-Managed Encryption Keys (CMEK) is the best choice.
Here’s why Option A is correct:
CMEK provides the highest level of control over encryption keys. It allows you to manage your own encryption keys, giving you the flexibility to meet your specific security and compliance requirements. This is especially important when dealing with sensitive data that needs to adhere to strict data privacy regulations.
Encryption at rest: Google Cloud Storage supports encryption of data at rest by default, but with CMEK, you gain the ability to use your own keys for encryption, offering greater control over who can access and decrypt the data.
Encryption in transit: Google Cloud ensures that data is encrypted in transit by default. However, the combination of CMEK for at-rest encryption and built-in encryption for transit ensures a comprehensive security approach.
Now, let’s look at the other options:
Option B (Google BigQuery with automatic encryption enabled): BigQuery does offer encryption by default, both in transit and at rest, but it does not offer the same level of control over encryption keys as CMEK in Cloud Storage. If the company requires management of encryption keys, this option might not meet the exact requirements.
Option C (Google Cloud Spanner with encryption at rest): While Cloud Spanner provides encryption at rest and strong security features, it may not offer the same flexibility as Google Cloud Storage with CMEK. If the primary requirement is around controlling access to sensitive datasets with encryption keys, Cloud Storage with CMEK is a better fit.
Option D (Google Cloud SQL with SSL/TLS encryption): Cloud SQL supports SSL/TLS encryption for data in transit and encryption at rest by default, but it doesn't provide the same level of fine-grained control over encryption keys as Cloud Storage with CMEK.
Thus, Option A provides the best solution for securing sensitive data with control over encryption keys and ensuring compliance.
Question No 10:
You are designing a solution for a company that needs to deploy a multi-region application on Google Cloud. The application must be resilient to regional outages and must maintain low-latency access to users in different geographical regions.
Which Google Cloud service would you use to ensure high availability and minimize latency for users?
A. Google Cloud Load Balancing with multiple backend services in different regions
B. Google Cloud CDN with regional instances of Compute Engine
C. Google Cloud Pub/Sub with multi-region delivery
D. Google Kubernetes Engine with multiple regional clusters
Correct Answer: A
Explanation:
The best approach to ensuring high availability and minimizing latency for users across multiple regions is to use Google Cloud Load Balancing with multiple backend services deployed in different regions.
Here’s why Option A is correct:
Global Load Balancing: Google Cloud Load Balancing provides a fully managed, global load balancing solution. It allows you to distribute traffic across multiple regions automatically, ensuring that traffic is routed to the closest and healthiest backend.
Regional Resilience: By deploying backend services in multiple regions, the application becomes resilient to regional failures. If one region experiences issues, the load balancer automatically reroutes traffic to another region with minimal downtime and latency.
Low-Latency Access: The load balancer uses a global HTTP(S) routing mechanism, ensuring that users are directed to the closest available region, minimizing latency. This is particularly beneficial for applications serving a global user base.
Let’s examine the other options:
Option B (Google Cloud CDN with regional instances of Compute Engine): While Google Cloud CDN can help reduce latency for static content by caching it closer to users, it does not handle dynamic traffic or multi-region resilience. Using CDN for dynamic applications is not the best fit for this use case.
Option C (Google Cloud Pub/Sub with multi-region delivery): Google Cloud Pub/Sub is designed for event-driven systems and messaging between services, but it is not a direct solution for load balancing web traffic and ensuring high availability and low latency for users across regions.
Option D (Google Kubernetes Engine with multiple regional clusters): While GKE can provide multi-region deployment, setting up and maintaining multiple Kubernetes clusters across regions can be complex and require additional management overhead compared to using a managed load balancing service.
Thus, Option A: Google Cloud Load Balancing with multiple backend services is the most straightforward and efficient choice for ensuring high availability, low latency, and resilience for a global application.
