Visit here for our full Google Professional Cloud Architect exam dumps and practice test questions.
Question 1
Which Google Cloud service allows you to store and retrieve structured relational data with high availability, strong consistency, and horizontal scaling?
A) Cloud Storage
B) Cloud Spanner
C) Cloud SQL
D) BigQuery
Answer: B
Explanation:
A Cloud Storage is an object storage service primarily designed for unstructured data such as images, videos, backups, or archives. It offers high durability and availability but does not support relational database features, ACID transactions, or strong consistency across globally distributed nodes. While it is ideal for large volumes of unstructured data, it is unsuitable for structured relational workloads requiring SQL-like operations or transactional integrity.
B Cloud Spanner is a fully managed, horizontally scalable relational database service. It combines the benefits of traditional relational databases, such as ACID transactions and SQL support, with the scalability and global distribution of NoSQL systems. Cloud Spanner ensures strong consistency across regions, automatic replication, high availability, and automated scaling for both reads and writes. It integrates with Google Cloud monitoring, IAM, and backup systems to ensure enterprise-grade reliability and security. Spanner is particularly suitable for applications requiring mission-critical performance, global distribution, and complex relational queries. Its horizontal scaling enables workloads to grow without downtime or performance degradation, making it a unique solution for high-demand relational data storage.
C Cloud SQL is a fully managed relational database service supporting MySQL, PostgreSQL, and SQL Server. It provides automated backups, maintenance, and patching but is limited to regional scaling and does not support global horizontal scaling or strong cross-region consistency like Cloud Spanner. Cloud SQL is suitable for smaller transactional workloads but may struggle with extremely high-volume, globally distributed applications.
D BigQuery is a serverless data warehouse optimized for analytical workloads, complex queries on massive datasets, and business intelligence. While it offers high-speed querying and supports SQL-like syntax, it is designed for analytics rather than transactional, structured, relational data with ACID guarantees. BigQuery is ideal for reporting, data analysis, and insights but not for operational database workloads requiring real-time consistency and transactions.Cloud Spanner provides a unique combination of relational structure, strong consistency, horizontal scalability, and high availability, making it ideal for enterprise-grade, mission-critical applications.
Question 2
Which Google Cloud service provides a fully managed environment for deploying, orchestrating, and scaling containerized applications with high availability?
A) Cloud Functions
B) App Engine
C) Kubernetes Engine (GKE)
D) Cloud Run
Answer: C
Explanation:
A Cloud Functions is a serverless platform for executing event-driven, lightweight functions in response to triggers such as HTTP requests, Pub/Sub messages, or Cloud Storage events. While it provides automatic scaling, it is designed for microservices or small tasks and lacks full container orchestration features, multi-container management, or complex networking configurations. It is best suited for event-driven workloads rather than production-grade containerized applications.
B App Engine is a fully managed platform for running applications without managing the underlying infrastructure. It handles automatic scaling, patching, and updates but is less flexible for deploying complex multi-container applications requiring custom orchestration, network control, or persistent storage configurations. App Engine is suitable for traditional web applications but cannot provide the full orchestration capabilities of Kubernetes Engine.
C Kubernetes Engine (GKE) is Google Cloud’s managed Kubernetes service that provides robust container orchestration. GKE supports deployment, scaling, load balancing, automatic updates, and monitoring of containerized applications. It integrates with Google Cloud IAM, Cloud Monitoring, Cloud Logging, and networking services to ensure high availability and security. GKE supports autoscaling of both nodes and pods, rolling updates, and zero-downtime deployments. Its flexibility allows organizations to manage complex multi-container applications, advanced networking, and persistent storage while maintaining resilience, high performance, and observability.
D Cloud Run is a serverless platform for running containerized applications in a stateless environment. It automatically scales containers and is easy to deploy, but it is optimized for simpler workloads and does not provide the advanced orchestration, multi-container management, or persistent networking features available in GKE.
Question 3
Which Google Cloud service allows real-time monitoring, logging, and visualization of metrics for applications and infrastructure across multiple environments?
A) Cloud Logging
B) Cloud Monitoring
C) Cloud Trace
D) Cloud Debugger
Answer: B
Explanation:
A Cloud Logging is a centralized platform for collecting, storing, and analyzing logs from applications and infrastructure. It helps troubleshoot issues, audit events, and track errors but focuses on log management rather than providing real-time dashboards, metric visualization, or proactive alerting. While essential for investigating problems, it does not offer full observability of application performance or system health.
B Cloud Monitoring provides comprehensive real-time observability for applications, virtual machines, databases, and cloud services. It collects metrics, visualizes them in dashboards, and supports alerting policies for proactive issue resolution. Cloud Monitoring can integrate with Cloud Logging, Cloud Trace, and Cloud Debugger to provide a complete picture of system performance. Organizations can track latency, availability, error rates, and resource utilization across multiple environments, enabling quick detection of anomalies. Automated integrations with incident management tools allow teams to respond effectively. Cloud Monitoring supports custom metrics, enabling teams to monitor business-critical KPIs in addition to technical metrics. Its dashboards and alerts ensure reliable performance and help maintain compliance with operational standards.
C Cloud Trace tracks latency and provides insights into request paths for performance analysis. While it is valuable for debugging slow applications, it does not provide full metric visualization or alerting capabilities required for continuous monitoring.
D Cloud Debugger allows developers to inspect live application code and variable states without stopping execution. It is primarily a debugging tool, not a comprehensive monitoring or metric visualization service.Cloud Monitoring ensures complete visibility, real-time alerting, and performance insights, helping organizations optimize applications and infrastructure effectively.
Question 4
Which Google Cloud service allows centralized management and enforcement of security policies, constraints, and governance across multiple projects and resources?
A) Cloud IAM
B) Organization Policy Service
C) Cloud Key Management Service
D) Security Command Center
Answer: B
Explanation:
A Cloud IAM (Identity and Access Management) allows administrators to grant roles and permissions to users, groups, and service accounts at the project, folder, or resource level. While IAM is essential for access control, it does not enforce organization-wide policy constraints across multiple projects, nor does it govern configuration compliance at scale.
B Organization Policy Service provides centralized governance by allowing administrators to define and enforce constraints across all projects under an organization. Policies can control service usage, prevent misconfigurations, enforce security standards, and maintain regulatory compliance. Examples include restricting certain APIs, controlling VM instance types, or preventing public IP exposure. Organization Policy ensures consistency across projects, provides auditing capabilities, and reduces operational overhead. It complements IAM by enforcing organization-wide rules in addition to individual permissions, providing a higher level of security and compliance management across the enterprise.
C Cloud Key Management Service (KMS) allows secure creation, storage, and rotation of cryptographic keys for encryption. While KMS is vital for data protection, it does not provide centralized policy enforcement or governance across multiple projects.
D Security Command Center is a security and risk management platform that identifies vulnerabilities, misconfigurations, and threats. While it provides visibility and recommendations, it does not enforce policies centrally or prevent configuration violations automatically.Organization Policy Service ensures centralized policy enforcement, governance, and compliance across all Google Cloud projects, reducing risk and operational complexity.
Question 5
Which Google Cloud service executes lightweight, event-driven functions triggered by HTTP requests, Pub/Sub messages, or Cloud Storage events in a fully serverless environment?
A) Cloud Functions
B) Cloud Run
C) App Engine
D) Kubernetes Engine
Answer: A
Explanation:
A Cloud Functions is a serverless platform designed for executing small, event-driven functions. It automatically scales in response to incoming events such as HTTP requests, Cloud Pub/Sub messages, or Cloud Storage events. Cloud Functions abstracts infrastructure management, allowing developers to focus entirely on code execution. It integrates with IAM for access control and Cloud Logging and Monitoring for observability. This service is ideal for microservices, event-driven tasks, lightweight APIs, or background processing pipelines. Cloud Functions supports multiple programming languages and allows versioning, rollback, and automated updates for safe deployments.
B Cloud Run executes containerized applications in a fully managed serverless environment. While it is excellent for stateless containers, it is optimized for complete container workloads rather than individual event-driven functions. Cloud Run offers HTTP triggers but lacks the fine-grained event-driven focus of Cloud Functions.
C App Engine is a fully managed platform for running web applications and services without managing infrastructure. It automatically scales and supports multiple runtimes but is designed for complete applications rather than discrete event-driven functions.
D Kubernetes Engine (GKE) orchestrates containers at scale with advanced networking, storage, and deployment capabilities. While it can run serverless workloads via Knative, GKE requires managing clusters and is more complex than the fully serverless Cloud Functions platform.Cloud Functions provides event-driven, fully serverless execution with automatic scaling, ideal for lightweight tasks and microservices workflows.
Question 6
Which Google Cloud service is best suited for building scalable data processing pipelines that use parallel execution to process large datasets efficiently?
A) Cloud Dataflow
B) Cloud Dataproc
C) Cloud Composer
D) Cloud Functions
Answer: A
Explanation:
A Cloud Dataflow is a fully managed service designed specifically for large-scale data processing tasks involving both stream and batch processing. It provides automatic scaling, parallel execution, and built-in optimization capabilities that allow jobs to handle extremely large datasets efficiently. Dataflow uses the Apache Beam programming model, enabling developers to write a single pipeline that runs in either streaming or batch mode. It supports complex data transformation tasks, windowing, sessionization, and event-time processing, making it a powerful choice for real-time analytics and ETL pipelines. The service manages infrastructure, autoscaling, and fault tolerance completely, allowing teams to focus on logic rather than cluster management. Its integration with Pub/Sub, BigQuery, Cloud Storage, and Bigtable makes it one of the most scalable and flexible tools for high-volume data workflows.
B Cloud Dataproc is a managed Hadoop and Spark service that simplifies running distributed data processing clusters. While it is useful for existing Hadoop/Spark workloads, it requires cluster management and is not fully serverless like Dataflow. Dataproc is suitable for organizations migrating legacy big data systems but does not offer the same level of automatic optimization or parallelization controls that Dataflow provides.
C Cloud Composer is an orchestration tool based on Apache Airflow. It automates workflow dependencies and scheduling but is not a data processing engine itself. Composer is used to coordinate data processing tasks, not perform the heavy data transformations or parallel execution required for large-scale ETL operations.
D Cloud Functions is a serverless compute platform for lightweight, event-driven execution. It is unsuitable for heavy data processing tasks, parallelized pipelines, or large dataset transformations. Functions are not designed to handle large, continuous data streams or batch processing workflows.Cloud Dataflow provides the most efficient, scalable, and fully managed platform for parallel, large-scale data processing pipelines, making it ideal for enterprises building robust ETL and real-time analytics processes.
Question 7
Which Google Cloud storage option is recommended for applications requiring extremely low-latency, high-throughput access to structured, non-relational key-value data?
A) Cloud SQL
B) Cloud Bigtable
C) Firestore
D) Cloud Storage Standard**
Answer: B
Explanation:
A Cloud SQL is a managed relational database service supporting PostgreSQL, MySQL, and SQL Server. It is optimized for traditional transactional workloads and relational schemas but does not offer the massive horizontal scalability or low-latency access patterns needed for extremely high-throughput key-value workloads. Cloud SQL is limited to regional deployment and vertical scaling, making it a poor fit for applications needing millisecond-level access at very large volumes.
B Cloud Bigtable is Google Cloud’s wide-column, NoSQL database designed specifically for massive workloads requiring very low latency and high throughput. It is ideal for time-series data, IoT workloads, recommendation systems, and key-value storage needs at petabyte scale. Bigtable supports automatic sharding, horizontal scaling, and seamless replication. Its architecture is optimized for single-digit millisecond response times, even during large-scale read/write operations. Bigtable integrates with Dataflow, Dataproc, and BigQuery for analytics, making it versatile across the data lifecycle. Because it is schema-less and horizontally distributed, it easily supports millions of operations per second with minimal operational overhead.
C Firestore is a document-based NoSQL database optimized for application development, mobile synchronization, and real-time updates. While it offers strong client SDK support and real-time capabilities, it is not optimized for high-throughput analytical or key-value workloads at massive scale like Bigtable. It is best suited for user-centric applications, mobile backends, and real-time apps rather than large analytical pipelines or huge operational datasets.
D Cloud Storage Standard is an object storage service used for unstructured data, such as backups, images, or large binary files. It is not designed for structured key-value queries, low-latency lookups, or high-throughput read/write operations. Object storage operates with different performance characteristics and is not suitable for database-style workloads.Cloud Bigtable is the optimal choice for very low-latency, high-volume key-value workloads, offering massive horizontal scalability and consistently fast performance.
Question 8
Which Google Cloud networking feature allows customers to implement a global load balancer that distributes traffic across multiple regions using a single anycast IP address?
A) Internal Load Balancing
B) Cloud CDN
C) Cloud Armor
D) Cloud Load Balancing**
Answer: D
Explanation:
A Internal Load Balancing is designed for distributing traffic within private networks, typically for back-end services inside a VPC. It supports only regional traffic distribution and cannot provide global traffic balancing or a single anycast IP address for worldwide access. Its use is primarily internal to private infrastructures, making it unsuitable for global, public-facing services.
B Cloud CDN is a content delivery network that caches content at edge locations to reduce latency. While it works together with Cloud Load Balancing, it does not itself perform traffic distribution or provide global load balancing capabilities. CDN accelerates content delivery but depends on load balancing for routing decisions, so it cannot function as a standalone global traffic distribution mechanism.
C Cloud Armor provides security features such as DDoS protection, WAF rules, and IP-based filtering. While critical for enhancing security posture, it is not a load balancing system and does not manage traffic distribution. Cloud Armor typically integrates with Cloud Load Balancing but does not perform routing or application-level traffic management.
D Cloud Load Balancing is a fully distributed global load balancing solution that uses a single anycast IP address to distribute traffic across multiple regions. It intelligently routes user requests based on proximity, health checks, and backend performance. Cloud Load Balancing supports HTTP(S), TCP/SSL, and UDP traffic, ensuring optimal availability and performance. It automatically scales with traffic and integrates with Cloud CDN, Armor, and Monitoring for end-to-end application management. Its global capability ensures seamless failover and optimized response times across the world.Cloud Load Balancing provides true global distribution with anycast IP support, making it ideal for high availability and low latency at global scale.
Question 9
Which Google Cloud service provides a fully managed extract, load, and transform (ELT) platform for analyzing large datasets using SQL in a serverless environment?
A) Cloud SQL
B) BigQuery
C) Cloud Data Fusion
D) Dataproc**
Answer: B
Explanation:
A Cloud SQL is a transactional relational database service designed for OLTP workloads. While it supports SQL queries, it is not optimized for analyzing massive datasets or running ELT processes at large scale. Cloud SQL offers limited storage and processing capabilities compared to analytical platforms like BigQuery, making it unsuitable for enterprise-level analytics.
B BigQuery is a serverless, highly scalable data warehouse that enables lightning-fast SQL-based analytics. It supports petabyte-scale datasets, automatic performance optimization, encryption by default, columnar storage, and built-in machine learning. BigQuery is fully managed, requiring no infrastructure management or cluster tuning. Its ELT-based design allows raw data to be loaded directly and transformed using SQL queries. BigQuery’s separation of storage and compute enables cost-efficient scaling while ensuring high performance. It integrates smoothly with Dataflow, Data Fusion, Dataproc, Pub/Sub, and machine learning workflows. Its powerful analytical engine makes it ideal for data warehousing, reporting, and business intelligence.
C Cloud Data Fusion is a managed data integration platform used to build ETL pipelines via a visual interface. While it facilitates data ingestion and transformation workflows, it is not the analytics engine itself. It typically delivers data into BigQuery, where large-scale analysis occurs.
D Dataproc is a managed Spark and Hadoop service used for existing big data clusters. It requires cluster management and tuning, making it less efficient and more costly than BigQuery for large-scale analytical queries. Dataproc is suited for organizations maintaining legacy systems but is not serverless or ELT-focused.BigQuery provides a serverless, massively scalable, SQL-based analytics platform, making it the top choice for large-scale data analysis.
Question 10
Which Google Cloud service centralizes security findings, threat intelligence, vulnerability data, and misconfiguration insights into a unified dashboard?
A) Cloud Logging
B) Cloud Armor
C) Security Command Center
D) Identity-Aware Proxy**
Answer: C
Explanation:
A Cloud Logging collects, stores, and analyzes logs from applications and infrastructure but does not provide unified threat intelligence, vulnerability scanning, or security posture assessments. While logs contribute to security monitoring, they do not provide consolidated insights or automated risk analysis. Cloud Logging is essential for observability but not a comprehensive security platform.
B Cloud Armor provides DDoS protection, WAF capabilities, custom security rules, and IP filtering for applications. Although it strengthens security at the network and application layers, it does not aggregate security findings or generate insights about misconfigurations or compliance issues. It is a protective tool rather than a holistic security management platform.
C Security Command Center (SCC) is Google Cloud’s unified security management and threat intelligence platform. It consolidates vulnerability findings, misconfiguration alerts, identity risks, threat detections, and compliance violations. SCC integrates with numerous Google Cloud services to collect security signals, helping organizations understand their overall risk posture. It includes features such as asset inventory, security health analytics, misconfiguration scanning, threat detection, and third-party integrations. SCC helps security teams quickly identify issues like exposed storage buckets, risky firewall rules, vulnerable VM configurations, IAM privilege escalation risks, and possible compromise indicators. It provides real-time monitoring capabilities and a centralized dashboard for reviewing and addressing security threats, making it essential for enterprise-level cloud security governance.
D Identity-Aware Proxy (IAP) provides secure access to applications using user identity and context but does not collect or aggregate threat intelligence or vulnerability data. IAP focuses on access control rather than security monitoring or risk analysis.Security Command Center delivers centralized visibility, risk management, and threat detection across Google Cloud environments.
Which service offers serverless event-driven data processing for events from Pub/Sub, Storage, or HTTP triggers?
A) Cloud Functions
B) Cloud Run
C) App Engine
D) Dataproc
Answer: A
Explanation:
A Cloud Functions is a fully serverless, event-driven compute service designed to run small pieces of code in response to events. It integrates deeply with services such as Pub/Sub, Firestore, Cloud Storage, and HTTP triggers. Cloud Functions automatically scales based on incoming event load without requiring infrastructure provisioning, cluster management, or capacity planning. It is ideal for lightweight event processing, real-time triggers, asynchronous tasks, IoT events, and micro-automation. Because the service charges only for actual execution time, it is highly cost-efficient for workloads where processing is intermittent or unpredictable. Functions support multiple runtimes, making it easy for developers to quickly deploy logic without managing servers.
B Cloud Run is also serverless but is designed to run containerized applications. While it can handle event-driven tasks via Pub/Sub or Eventarc, it is better suited for full applications, APIs, or containerized workloads rather than small function-level event processing. Cloud Run offers more flexibility but is not as lightweight as Cloud Functions for simple event triggers.
C App Engine provides a PaaS environment for deploying web applications. Although it supports HTTP-triggered events, it is not built for granular event-driven execution and is less efficient for simple trigger-based tasks. It is better for long-running services and complete application backends.
D Dataproc is a managed Hadoop/Spark service used for big data processing, not event-driven computing. It is cluster-based, requires provisioning, and is meant for analytics workloads—not for responding to Pub/Sub or Storage events in real time.Cloud Functions remains the top choice for pure, serverless, event-driven execution without infrastructure management.
Question 12
Which service offers centralized log storage, filtering, and analysis for all Google Cloud resources?
A) Cloud Logging
B) BigQuery
C) Cloud Trace
D) Cloud Monitoring
Answer: A
Explanation:
A Cloud Logging is Google Cloud’s fully managed logging solution that collects logs from compute resources, VPC networks, Kubernetes workloads, API calls, and custom applications. It provides a unified dashboard where administrators can search, filter, export, and analyze logs in real time. Cloud Logging supports log routing to BigQuery, Pub/Sub, and Storage for long-term retention and advanced analytics. It also enables log-based metrics that integrate directly with Cloud Monitoring for alerting. The system automatically indexes logs, supports structured/semi-structured formats, and provides powerful querying tools. With high durability and rapid ingestion, Cloud Logging is essential for operational diagnostics, security auditing, debugging, and compliance tracking across cloud environments.
B BigQuery is an analytics data warehouse. While logs can be exported to BigQuery for analysis, BigQuery does not collect logs on its own. It is used only when large-scale log analytics is needed, not as a centralized logging platform.
C Cloud Trace is used for distributed tracing of application latency. While helpful for performance debugging, it does not store general logs or provide log filtering capabilities. Its focus is latency visualization, not log management.
D Cloud Monitoring collects metrics and performance indicators, not logs. Although Monitoring can use log-based metrics, it cannot function as a log storage or filtering system.Cloud Logging is the correct answer because it provides complete, centralized log collection and management.
Question 13
Which service provides fully managed APIs to automate and orchestrate multi-step data workflows?
A) Cloud Composer
B) Cloud Functions
C) Cloud Run
D) Pub/Sub
Answer: A
Explanation:
A Cloud Composer is Google Cloud’s fully managed workflow orchestration service built on Apache Airflow. It enables building, scheduling, and automating complex multi-step pipelines involving various services such as BigQuery, Dataflow, Dataproc, Cloud Storage, and external APIs. Composer allows tasks to run in sequence or parallel, includes dependency management, retries, SLA monitoring, and integrated logging. Because it uses Airflow DAGs (Directed Acyclic Graphs), it is ideal for long-running ETL workflows, data engineering pipelines, and cross-service automations. Composer manages the Airflow environment, updates, scaling, and security configurations automatically in a controlled manner.
B Cloud Functions can trigger tasks but is not suitable for orchestrating multi-step pipelines with dependencies, retries, and scheduling. It lacks workflow visualization and long-running orchestration capabilities.
C Cloud Run can run containers but does not provide workflow control or orchestration logic. It is useful for APIs or microservices but cannot coordinate multi-step workflows by itself.
D Pub/Sub offers messaging and event distribution but does not manage orchestration logic. It supports asynchronous communication but cannot define workflow dependencies or steps.Cloud Composer is best for scheduling and orchestrating multi-step workflows across multiple systems.
Question 14
Which service is the best choice for globally distributed relational data with strong consistency?
A) Cloud SQL
B) Cloud Spanner
C) BigQuery
D) Firestore
Answer: B
Explanation:
A Cloud SQL is a regional relational database that supports MySQL, PostgreSQL, and SQL Server. It offers strong consistency but cannot scale globally with multi-region write capabilities. Cloud SQL is limited by vertical scaling and is suitable for traditional relational workloads but not for global, horizontally scaled systems.
B Cloud Spanner is designed for global relational workloads requiring strong consistency, horizontal scaling, and high availability. Spanner combines the scalability of NoSQL with relational features such as SQL queries and schemas. It supports multi-region replication, distributed transactions, and industry-leading consistency guarantees. Spanner uses Google’s TrueTime API to maintain global clock synchronization, allowing consistent reads worldwide. It is ideal for financial systems, global inventory, e-commerce platforms, and mission-critical enterprise applications requiring near-zero downtime.
C BigQuery is an analytical database used for OLAP queries. It is not built for operational transactions or globally consistent relational workloads. It is columnar and optimized for large analytical scans, not for real-time relational consistency.
D Firestore is a distributed NoSQL document database with strong consistency but is not suited for relational workloads requiring complex joins or schemas. It supports multi-region deployments but is designed for application data, not heavy enterprise relational workloads.Cloud Spanner uniquely provides global scale, strong consistency, and relational capabilities, making it the best solution.
Question 15
Which service helps detect application latency issues with distributed tracing?
A) Cloud Trace
B) Cloud Debugger
C) Cloud Monitoring
D) Cloud Logging
Answer: A
Explanation:
A Cloud Trace is a fully managed distributed tracing system that collects, analyzes, and visualizes latency data from applications running on Google Cloud and other environments. It traces individual requests across multiple services, providing end-to-end visibility into application performance and helping engineers understand how requests flow through microservices, serverless functions, containers, or traditional VMs. Cloud Trace identifies bottlenecks, slow operations, and performance patterns over time by capturing detailed timelines of requests, including network delays, database query execution times, external API calls, and function execution latency. This allows teams to pinpoint which components are responsible for slow response times and to optimize system performance proactively. Cloud Trace integrates seamlessly with Cloud Run, Google Kubernetes Engine (GKE), App Engine, and custom applications via instrumentation libraries. Its sampling mechanisms ensure minimal overhead while providing actionable insights across high-volume production systems. Trace also supports integration with Cloud Monitoring and Cloud Logging, enabling correlation of traces with metrics and logs for comprehensive observability. It is particularly valuable in microservices architectures, where individual requests often pass through multiple services and traditional monitoring tools cannot reveal the exact cause of latency spikes. By visualizing requests as end-to-end traces, developers can prioritize performance improvements, optimize service dependencies, and improve user experience.
B Cloud Debugger attaches to running applications and allows developers to capture snapshots of variables and code execution without stopping the application. It is effective for debugging logic errors or inspecting state in production but does not provide latency measurements or trace requests across services.
C Cloud Monitoring collects metrics such as CPU usage, memory consumption, uptime, and log-based metrics. It can alert on performance anomalies but does not visualize request flows or identify internal call latency that contributes to slow performance.
D Cloud Logging centralizes application and system logs for search, analysis, and alerting. While useful for auditing and troubleshooting, logs alone cannot visualize distributed request traces or identify specific service-level latency issues.Cloud Trace is purpose-built for detecting, analyzing, and visualizing latency issues in complex, distributed applications, making it essential for optimizing performance in microservices and multi-component architectures.
Question 16
Which Google Cloud service is best suited for handling real-time stream processing at scale with autoscaling capabilities?
A) Cloud Dataflow
B) Dataproc
C) BigQuery
D) Cloud Functions
Answer: A
Explanation:
A Cloud Dataflow is a fully managed service designed specifically for both real-time streaming and batch data processing. It provides horizontal autoscaling, dynamic work rebalancing, windowing operations, watermark management, and exactly-once processing guarantees, making it ideal for large-scale continuous data streams. Dataflow handles data ingestion from Pub/Sub, IoT streams, messaging systems, and log pipelines, enabling transformations, enrichment, anomaly detection, and event processing. Because it supports the Apache Beam SDK, it allows developers to write a single pipeline that runs in both batch and stream modes. Its autoscaling ensures consistent performance even when traffic fluctuates rapidly, making it the best choice for real-time workloads.
B Dataproc is based on Hadoop and Spark, and while it can handle streaming jobs such as Spark Streaming or Flink, it requires cluster management and manual scaling. This limits its ability to handle rapid workload spikes. Dataproc is more suited for batch analytics, ETL, and big data workloads rather than low-latency stream processing.
C BigQuery is an analytical data warehouse optimized for large-scale SQL analytics. Although it can process streaming inserts, it is not a stream-processing engine and cannot provide real-time transformations or event-level processing. Its purpose is analysis, not real-time stream transformation.
D Cloud Functions can process event-driven tasks, but it is not designed for continuous or large-volume real-time data pipelines. It lacks windowing, watermarking, and large-scale transformation capabilities, making it unsuitable for complex streaming logic.Cloud Dataflow remains the most appropriate service for high-throughput, autoscaling, real-time stream processing.
Question 17
Which storage option provides the lowest latency and highest IOPS for mission-critical applications requiring block storage?
A) Persistent Disk
B) Local SSD
C) Cloud Storage
D) Filestore
Answer: B
Explanation:
A Persistent Disk is a durable and flexible block storage option for Compute Engine and GKE. It provides good performance with high reliability, snapshots, and resizing capabilities. However, it cannot match the extreme IOPS and ultra-low latency provided by Local SSD. Persistent Disk is optimized for general-purpose workloads rather than the most demanding transactional systems.
B Local SSD offers the highest IOPS and the lowest latency available on Compute Engine instances, making it ideal for workloads requiring extremely fast data access such as in-memory databases, high-frequency trading applications, and heavy transactional systems. Local SSD is physically attached to the virtual machine host, allowing microsecond-level latency. However, it is ephemeral, meaning data does not persist after VM termination, so it is best used for temporary, high-speed workloads.
C Cloud Storage is an object storage service for unstructured data. It provides high durability and availability but is not designed for block-level access or low-latency transactional workloads. It is commonly used for backups, media storage, analytics, and general file hosting.
D Filestore is a managed NFS-based file storage service. While it provides strong throughput for file-based workloads, it cannot achieve the ultra-high IOPS required for mission-critical, low-latency block storage use cases. It is more suitable for shared file systems and enterprise applications like content management systems.Local SSD remains the best solution for ultra-fast, high-performance block storage requirements.
Question 18
Which Google Cloud service provides a fully managed relational database with automatic backups, patches, and replicas?
A) Cloud SQL
B) Cloud Spanner
C) Bigtable
D) Memorystore
Answer: A
Explanation:
Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server. It automatically handles routine database management tasks, including backups, replication, failover, maintenance, patching, and storage scaling, significantly reducing operational overhead for database administrators. Cloud SQL is designed for transactional workloads that require relational schema, SQL queries, and ACID compliance. Typical use cases include web applications, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, content management platforms, and any frameworks that rely on SQL-based storage. Cloud SQL also provides high availability configurations with automated failover, read replicas for horizontal scaling of read-intensive workloads, and point-in-time recovery to ensure data durability. It integrates seamlessly with Compute Engine, Google Kubernetes Engine (GKE), and serverless platforms such as Cloud Functions and App Engine, making it suitable for a wide range of application architectures. Security features include encryption at rest and in transit, IAM-based access controls, private IP connectivity, and automatic patching of vulnerabilities, helping organizations meet compliance requirements. Its fully managed nature allows developers to focus on building applications rather than maintaining database infrastructure.
Cloud Spanner is a globally distributed relational database designed for horizontal scaling and multi-region writes. While it provides strong consistency and high availability across continents, it is more complex and unnecessary for typical relational applications that do not require global scale.
Bigtable is a NoSQL wide-column database built for large-scale analytical and operational workloads. It supports high throughput and low latency but does not provide SQL queries, relational schema, or ACID transactions, making it unsuitable for traditional relational workloads.
Memorystore is an in-memory caching service for Redis and Memcached. It is optimized for session storage, caching, and accelerating application performance but cannot function as a relational database or provide persistent SQL storage.For managed relational databases with automated maintenance, built-in reliability, and seamless integration with Google Cloud services, Cloud SQL remains the ideal choice.
Question 19
Which service provides a fully managed messaging system for asynchronous event communication between distributed systems?
A) Pub/Sub
B) Cloud Tasks
C) Cloud Functions
D) Eventarc
Answer: A
Explanation:
A Pub/Sub is a highly scalable, global, fully managed messaging service designed for asynchronous event delivery. It enables reliable communication between decoupled applications and services by allowing publishers to send messages to topics, which subscribers can then receive in real time. Pub/Sub provides durable message persistence, ensuring that messages are not lost even if subscribers are temporarily offline. It guarantees at-least-once delivery and supports horizontal scaling to handle millions of messages per second, making it ideal for enterprise-grade workloads and globally distributed systems. Its flexibility allows it to support a wide variety of use cases, including microservices communication, data pipeline ingestion, event-driven architectures, Internet of Things (IoT) telemetry, real-time analytics, and notifications. Pub/Sub decouples senders and receivers, reducing system interdependencies and improving reliability, maintainability, and scalability. Additionally, Pub/Sub integrates seamlessly with other Google Cloud services such as Cloud Functions, Dataflow, BigQuery, and Eventarc, enabling event-driven workflows and stream processing with minimal operational overhead. Its high throughput, low latency, and managed infrastructure make it a preferred backbone for building scalable, resilient, and event-driven cloud applications.
B Cloud Tasks is a managed service for executing background jobs and asynchronous tasks with fine-grained control over rate limiting, scheduling, and retries. While it is suitable for controlling task execution in microservices or API workloads, it does not provide a publish/subscribe messaging model. Cloud Tasks is designed for job execution management rather than distributing messages to multiple subscribers.
C Cloud Functions is a serverless compute platform that can consume messages from Pub/Sub topics to trigger event-driven workflows. However, it is not a messaging system itself. Cloud Functions focuses on executing code in response to events rather than providing scalable, durable message distribution to multiple subscribers.
D Eventarc provides event routing across Google Cloud services, including Pub/Sub and other event sources. While it allows routing and orchestration of events, it relies on underlying systems such as Pub/Sub for actual message delivery and does not function as a standalone messaging backbone.For scenarios requiring large-scale, reliable, and asynchronous message distribution across multiple subscribers, Pub/Sub remains the optimal choice, providing a resilient, globally available, and fully managed messaging platform suitable for modern cloud-native architectures.
Question 20
Which Google Cloud service allows you to expose a VM-based application over HTTPS with built-in SSL termination and global load balancing?
A) Cloud Load Balancing
B) Cloud Armor
C) Cloud CDN
D) Cloud VPN
Answer: A
Explanation:
A Cloud Load Balancing provides global load balancing with support for HTTP and HTTPS traffic, SSL termination, cross-region failover, and autoscaling. It can distribute requests across virtual machines, containers, and serverless environments, ensuring high availability and optimal performance. By terminating SSL at the load balancer, it offloads encryption overhead from backend servers, improving application response times. Cloud Load Balancing integrates with Cloud Armor for security, Cloud CDN for content caching, and custom routing policies to optimize performance and reliability. It is ideal for exposing VM-based or containerized applications on a secure, global, and highly available endpoint.
B Cloud Armor provides security controls such as Web Application Firewall (WAF) rules, DDoS mitigation, and IP allow/block lists. While it enhances application security and integrates with Cloud Load Balancing, it cannot distribute traffic, terminate SSL, or perform load balancing functions. Its role is complementary, protecting the applications rather than directing client requests.
C Cloud CDN accelerates content delivery by caching static content at edge locations close to users. It reduces latency and improves performance for static assets like images, videos, and scripts. Although Cloud CDN integrates with load balancers to serve cached content efficiently, it does not handle traffic distribution, cross-region failover, or SSL termination on its own.
D Cloud VPN creates secure tunnels between on-premises networks and Google Cloud, enabling encrypted connectivity over public networks. It is designed for connecting private networks rather than exposing applications to public HTTPS endpoints. Cloud VPN does not provide global load balancing, SSL termination, or application traffic distribution.Cloud Load Balancing is the correct choice for distributing HTTPS traffic with global availability, SSL termination, and integration with security and performance services like Cloud Armor and Cloud CDN.
