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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: 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 |
| 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 - VPC design and configuration - Express Connect and VPN Gateway |
| Topic 5: Building Highly Available, Performant Cloud Architecture | 22% | - Performance optimization and resilience design - Auto Scaling and Server Load Balancer (SLB) - Multi-zone deployment and disaster recovery |
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NEW QUESTION # 13
Muthu is a Cloud Architect who is designing the architecture of a new application being deployed to Alibaba Cloud. The application will run on Elastic Compute Service (ECS) pay-as-you-go instances and will automatically scale across multiple zones based on load. The ECS cluster will scale in and out frequently throughout the day. An Application Load Balancer (ALB) will handle the load distribution. The architecture needs to support distributed session data management.
What should Muthu do to ensure that the architecture supports distributed session data management?
Answer: A
Explanation:
Tair (Redis OSS-Compatible) is the appropriate centralized session-state layer for an elastic, distributed application. When ECS instances frequently scale in and out, application sessions must not depend on the lifecycle of any individual compute node. By storing session tokens and session state in Tair, every ECS instance can retrieve the same session information, making the web/application tier effectively stateless and compatible with Auto Scaling.
Alibaba Cloud Tair is a fully managed Redis-compatible database designed for high throughput, low latency, elastic scalability, and high availability. Alibaba Cloud also documents session-oriented Tair use cases in which session tokens are stored as Redis/Tair data structures with TTL values, demonstrating precisely why the service fits distributed session management.
Session stickiness is weaker because it attempts to keep a user on a specific backend and therefore creates affinity to an instance that might be removed during scaling or failover. CloudMonitor is an observability service, not a session store. STS issues temporary Alibaba Cloud credentials and does not maintain application user sessions.
Consequently, separating session state from ECS compute by storing it in Tair gives the architecture the elasticity and fault tolerance required.
Study Guide reference: Distributed Architecture on Alibaba Cloud; Performant Architecture; Core Infrastructure Deep Dive.
NEW QUESTION # 14
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: B,C
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 # 15
A media company is developing a video processing pipeline utilizing Alibaba Cloud Container Service for Kubernetes (ACK). They want to use a serverless approach to accommodate unpredictable workloads but are unsure about its impact on logging.
What considerations should the company keep in mind regarding logging in a serverless ACK environment?
Answer: A
Explanation:
Simple Log Service (SLS) is the appropriate logging platform for an ACK Serverless environment. Serverless Kubernetes removes much of the node-management responsibility, so the logging architecture should follow the same managed-service model rather than introduce dedicated log-processing infrastructure.
Alibaba Cloud provides native SLS integration for ACK Serverless. SLS can collect container standard output and application log files and then provide centralized search, analysis, dashboards, alerting, and downstream shipping. Alibaba documentation specifically states that ACK Serverless provides managed SLS capabilities and supports collecting container logs directly into SLS.
ARMS is primarily an application performance monitoring and observability platform; it does not replace SLS as the core centralized log-ingestion service. Option C is incorrect because SLS supports near-real-time collection and analysis. Option D also misunderstands serverless logging: scaling events do not inherently require dedicated logging servers.
The architecture should therefore preserve the operational advantages of serverless compute by using SLS as a fully managed, elastic logging layer capable of handling workload fluctuations without requiring the organization to provision and administer separate logging infrastructure.
Study Guide reference: Core Infrastructure Deep Dive - ACK, serverless container architecture, observability, and Simple Log Service.
NEW QUESTION # 16
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: B,C,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 # 17
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: D
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 # 18
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