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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing Workloads on Alibaba Cloud | 22% | - WAF, Anti-DDoS and network protection - Resource Access Management (RAM) and identity control - Data security and compliance best practices |
| Topic 2: Delivering Services and Content on Alibaba Cloud | 12% | - Service integration and access optimization - CDN and content delivery strategies - Alibaba Cloud DNS and domain management |
| Topic 3: Core Infrastructure Deep Dive | 26% | - File Storage NAS and block storage - Elastic Compute Service (ECS) and serverless computing - Object Storage Service (OSS) and storage solutions |
| Topic 4: Building Enterprise-grade Networks on Alibaba Cloud | 18% | - Network routing and security groups - Express Connect and VPN Gateway - VPC design and configuration |
| Topic 5: Building Highly Available, Performant Cloud Architecture | 22% | - Auto Scaling and Server Load Balancer (SLB) - Performance optimization and resilience design - Multi-zone deployment and disaster recovery |
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NEW QUESTION # 10
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 # 11
Ibrahim manages a large-scale manufacturing plant equipped with state-of-the-art instrumentation and telemetry that collects various information about the machines and products. These sensors collectively generate 1 TB worth of logs each day. Each notification is approximately 2 KB in size. You have been contracted to design a solution to ingest and store these logs for future analysis.
Ibrahim wants a highly available solution, but also wants to minimize costs. Additionally, he does not want to manage additional infrastructure. To meet their business requirements, they need to keep 14 days of data available for immediate analysis, and archive any other data older than 14 days.
What is the MOST operationally efficient solution that meets these requirements?
Answer: C
Explanation:
Simple Log Service (SLS) is a managed log-ingestion, storage, query, analysis, and visualization service, making it the most operationally efficient front-end for this telemetry workload. It eliminates the requirement to provision and manage ECS-based collectors or a separate search cluster simply to ingest and analyze log records.
For longer-term retention, Alibaba Cloud supports automatically shipping SLS data to OSS. This creates a clean hot-to-cold architecture: maintain recent data in SLS for interactive analysis and export older data to OSS for economical retention.
OSS lifecycle management can then automatically transition aging objects to lower-cost storage classes such as Archive. Lifecycle rules are explicitly designed to automate storage-class transitions based on object age, eliminating manual archival administration.
Option A requires additional Elasticsearch retention administration. Option B requires managing ECS infrastructure even though the question explicitly prioritizes reduced operational management. Option D adds Kafka unnecessarily and still specifies manual archival, directly conflicting with the requirement to minimize operational effort.
Thus, SLS for managed ingestion and recent analysis combined with SLS-to-OSS shipping and OSS lifecycle transitions provides the strongest serverless/managed retention architecture.
Study Guide reference: Core Infrastructure Deep Dive; Core Storage Infrastructure; operationally efficient log and data-storage architecture.
NEW QUESTION # 12
Sandra hosts a web application on a single ECS instance that stores documents uploaded by users in an EBS data disk. As the requirements grow, they duplicate the application and host it in a second ECS instance in a different zone. An ALB instance is used to distribute traffic between these ECS instances. However, after completing this change, users reported that every time they access the web application, they could only see some of their documents at any given time, but never all of the documents at the same time.
As a Cloud Architect, how would you solve this issue?
Answer: D
Explanation:
The architectural defect is that application state is stored independently on local block storage attached to each ECS instance. ALB can route successive requests to either backend, so each server exposes only the files stored on its own EBS disk. The correct solution is therefore to separate persistent shared data from individual compute instances.
Alibaba Cloud File Storage NAS provides shared file storage that can be mounted concurrently by multiple ECS instances. A single NAS file system becomes the authoritative repository for uploaded documents, so regardless of which ECS instance receives a request, both instances access the same underlying dataset.
Alibaba Cloud explicitly supports mounting NAS on multiple ECS instances for shared access.
Periodic EBS synchronization is operationally fragile and introduces consistency windows. ALB session persistence would merely hide the architectural problem and would fail when an ECS instance becomes unavailable or is replaced. Sending every request to both servers would also complicate application logic and still leave divergent storage.
Moving the data to NAS converts the application tier into a horizontally scalable compute layer while maintaining one consistent shared filesystem.
Study Guide reference: Core Infrastructure Deep Dive - ECS storage patterns, File Storage NAS, shared storage, and stateless application architecture.
NEW QUESTION # 13
A company is deploying a mission-critical application on Alibaba Cloud ECS instances that require high availability and failover capabilities. The application must remain accessible even if an entire zone fails.
Which deployment strategy should the company use to ensure high availability?
Answer: B
Explanation:
A multi-zone deployment within the same region is the standard architecture for surviving the complete failure of one availability zone. Alibaba Cloud zones within a region are physically isolated for fault tolerance while maintaining high-speed internal network connectivity. Alibaba Cloud explicitly recommends deploying resources across different zones when higher disaster-recovery capability is required.
For an application tier, multiple ECS instances should be placed in separate zones and normally fronted by a multi-zone ALB or another suitable load-balancing service. Health checks ensure that traffic is no longer directed to unavailable instances, allowing surviving instances in another zone to continue serving requests.
Alibaba Cloud ' s current high-availability guidance specifically describes multi-zone ECS and load-balancer deployments as the architecture for resilience against data-center or zone failures.
Option A remains vulnerable because all compute resources share the same zone failure domain. Option C retains a single compute point of failure regardless of storage performance. Option D can protect against an entire regional disaster, but it adds substantially greater networking, data synchronization, routing, and operational complexity than is necessary for the stated requirement of surviving one zone failure.
Therefore, distributing ECS instances across multiple zones within one region is the correct high-availability strategy.
Study Guide reference: Highly Available Architecture on Alibaba Cloud; Core Compute Infrastructure; multi- zone disaster recovery.
NEW QUESTION # 14
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 # 15
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