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| Section | Weight | Objectives |
|---|---|---|
| Core Infrastructure Deep Dive | 26% | - Object Storage Service (OSS) and storage solutions - Elastic Compute Service (ECS) and serverless computing - File Storage NAS and block storage |
| Building Highly Available, Performant Cloud Architecture | 22% | - Auto Scaling and Server Load Balancer (SLB) - Multi-zone deployment and disaster recovery - Performance optimization and resilience design |
| Building Enterprise-grade Networks on Alibaba Cloud | 18% | - Network routing and security groups - VPC design and configuration - Express Connect and VPN Gateway |
| Delivering Services and Content on Alibaba Cloud | 12% | - CDN and content delivery strategies - Alibaba Cloud DNS and domain management - Service integration and access optimization |
| Securing Workloads on Alibaba Cloud | 22% | - Data security and compliance best practices - Resource Access Management (RAM) and identity control - WAF, Anti-DDoS and network protection |
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NEW QUESTION # 24
A startup is building a serverless backend for their mobile application on Alibaba Cloud. The backend includes APIs for user authentication, data storage, and real-time notifications. The startup expects variable traffic with unpredictable spikes and wants to minimize costs by paying only for actual usage.
Which combination of Alibaba Cloud services should the startup use to achieve this architecture? (Correct answers: 2)
Answer: A,D
Explanation:
Function Compute and Simple Message Queue form an appropriate serverless, event-driven backend for bursty mobile workloads.
Function Compute is fully managed and automatically allocates elastic compute resources when functions are invoked. Alibaba Cloud bills Function Compute primarily according to actual configured resources and execution duration, allowing the startup to avoid maintaining continuously running application servers. This is particularly suitable for APIs and backend handlers experiencing unpredictable request spikes.
SMQ provides managed asynchronous messaging for decoupling backend components. It supports queue- based processing and publish-subscribe topics, making it suitable for notification events, background jobs, workflow messages, and traffic buffering. Alibaba Cloud explicitly identifies load leveling during sudden traffic spikes as an SMQ use case.
SMQ can also directly trigger Function Compute through queue or topic integrations, creating an event-driven design without dedicated message-consumer servers.
ECS requires persistent server resources and management, which conflicts with the requested serverless operating model. MaxCompute is a large-scale data warehouse and batch analytics platform rather than the application ' s transactional API or messaging layer.
Therefore, Function Compute supplies elastic execution while SMQ provides reliable asynchronous communication and notification-event handling.
Study Guide reference: Delivering Services and Content on Alibaba Cloud - Function Compute, serverless architectures, SMQ, event-driven integration, and elastic application services.
NEW QUESTION # 25
Liam ' s company operates an on-premises data analytics platform across 12 servers. The system runs scheduled jobs (70% of the time, each job taking anywhere from 20 minutes to 2 hours to complete, with tight SLAs) and user jobs (30% of the time, each job taking less than 5 minutes, with no SLA requirements).
Scheduled jobs must continue to meet SLAs during system failures, while user jobs can be delayed.
As a Cloud Architect, you are tasked to migrate the system to Alibaba Cloud ECS using a consumption-based model while maintaining the same high availability and SLA requirements.
Which solution is the MOST cost-effective and flexible?
Answer: C
Explanation:
Option A offers the strongest combination of resilience, predictable baseline capacity, flexibility, and reduced cost. Nine standard pay-as-you-go ECS instances are spread evenly across three zones and can support the scheduled workloads that have strict SLA requirements. The remaining three instances use lower-cost preemptible/spot capacity for user jobs that can tolerate interruption or delay.
Alibaba Cloud describes spot instances as offering the same compute performance as ordinary pay-as-you-go ECS but at substantially lower prices, with the trade-off that capacity can be reclaimed. This makes spot capacity appropriate for interruption-tolerant workloads but inappropriate as the sole capacity supporting strict SLAs.
A Savings Plan discounts eligible pay-as-you-go ECS usage while preserving significantly greater flexibility than traditional reserved-instance matching. Current Alibaba Cloud documentation states that Savings Plans apply discounts to pay-as-you-go usage and can offer flexibility across instance configurations; they do involve a one- or three-year hourly spend commitment, but they do not provision or reserve fixed ECS capacity.
Options C and D concentrate too much reliable capacity into fewer zones or place excessive workload on interruptible capacity. Option A therefore provides the best workload-tiering and multi-zone design among the available choices.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - multi-zone ECS, workload resilience, Savings Plans, and spot instances.
NEW QUESTION # 26
An online education company plans to migrate their services to Alibaba Cloud. They want to improve their video streaming quality during peak hours using Alibaba Cloud services. As a Cloud Architect, you are tasked with building a solution to manage traffic spikes without compromising on video quality.
What combination of services would BEST fit their requirements? (Correct answers: 3)
Answer: A,B,C
Explanation:
CDN should form the content-delivery layer for cacheable video assets. Alibaba Cloud CDN distributes content to geographically dispersed edge nodes so viewers retrieve cached objects from nearby POPs rather than repeatedly connecting to a distant origin. Alibaba Cloud explicitly supports video-oriented CDN acceleration and edge delivery, reducing both end-user latency and origin bandwidth demand.
Auto Scaling addresses peak-time compute demand. It monitors workload conditions and automatically increases ECS capacity during traffic spikes, then scales resources back when traffic decreases. Alibaba Cloud specifically identifies streaming platforms with unpredictable demand as a typical Auto Scaling scenario.
Global Accelerator complements CDN and Auto Scaling where viewers or application endpoints are separated by long-distance or cross-border networks. GA routes traffic through Alibaba Cloud ' s optimized global BGP network, reducing latency, jitter, and packet loss. Alibaba documentation explicitly identifies video streaming as a latency-sensitive scenario that benefits from this architecture.
Tair can improve authentication or session performance, but it does not directly address video delivery quality or regional network performance. Therefore, CDN + Auto Scaling + Global Accelerator most directly satisfies the streaming requirement.
Study Guide reference: Delivering Services and Content on Alibaba Cloud - CDN, Global Accelerator, Auto Scaling, video delivery, and global traffic optimization.
NEW QUESTION # 27
Kenneth plans to deploy a real-time data processing platform on Alibaba Cloud to support his company ' s Internet-of-Things (IoT) business. The platform will handle millions of sensor data points collected from smart devices globally. Kenneth needs to ensure high throughput and low latency for data ingestion and analysis.
Which solutions should Kenneth consider integrating into the platform architecture to meet his expected performance requirements? (Correct answers: 3)
Answer: A,B,D
Explanation:
Time Series Database is specifically designed for timestamped telemetry generated by IoT devices, monitoring systems, and industrial equipment. Alibaba Cloud identifies IoT device monitoring as a core TSDB scenario, including continuously collecting, storing, analyzing, and querying device-status and business-event data in real time.
ApsaraMQ for Kafka provides the high-throughput ingestion and buffering layer. It is a managed distributed Kafka platform intended for real-time data pipelines, monitoring-data aggregation, log collection, streaming processing, and large-scale analytics. Its partitioned distributed architecture lets producers ingest very large event volumes while downstream consumers process the streams independently.
Function Compute supplies elastic event processing without persistent compute servers. Alibaba Cloud supports ApsaraMQ for Kafka triggers that invoke Function Compute automatically whenever new records arrive in Kafka topics. This enables functions to perform transformations, filtering, enrichment, alert generation, or routing in near real time.
EMR is valuable for Hadoop/Spark-style batch analytics, but the question specifically emphasizes low-latency real-time ingestion and analysis. It therefore does not replace the streaming components selected above.
Study Guide reference: Core Infrastructure Deep Dive - IoT telemetry, TSDB, ApsaraMQ for Kafka, Function Compute, and real-time streaming architectures.
NEW QUESTION # 28
A worldwide retail company is adapting their e-commerce platform to handle spikes in online traffic during annual sales events. They are using Alibaba Cloud services and need a solution that can automatically scale resources to maintain performance during these peaks.
What solutions should the company implement to efficiently manage their resources during high-traffic periods? (Correct answers: 3)
Answer: A,C,D
Explanation:
The strongest architecture for an existing e-commerce platform experiencing predictable but substantial traffic spikes is OSS/CDN + ALB + Auto Scaling. Auto Scaling is specifically designed to increase ECS capacity when traffic or resource utilization rises and remove unnecessary instances after demand subsides. Alibaba Cloud documents this as a core mechanism for handling traffic spikes while avoiding permanent over- provisioning.
ALB complements Auto Scaling by distributing requests across healthy ECS backend instances. As scaling creates additional instances, they can be automatically added to the ALB server group, allowing traffic to use the enlarged compute pool immediately.
Static images and videos should be moved to OSS and accelerated through CDN. CDN caches static resources on geographically distributed edge nodes, substantially reducing requests reaching the ECS application tier and lowering origin bandwidth and processing load during sales events.
Function Compute is highly elastic, but introducing it merely to supplement an existing server-based application normally requires application restructuring. It is therefore less directly applicable than the three mechanisms specifically designed to scale and offload the existing architecture.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - Auto Scaling, ALB, CDN offloading, and elastic ECS architecture.
NEW QUESTION # 29
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