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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core Infrastructure Deep Dive | 26% | - Elastic Compute Service (ECS) and serverless computing - File Storage NAS and block storage - Object Storage Service (OSS) and storage solutions |
| Topic 2: Delivering Services and Content on Alibaba Cloud | 12% | - CDN and content delivery strategies - Service integration and access optimization - Alibaba Cloud DNS and domain management |
| Topic 3: Securing Workloads on Alibaba Cloud | 22% | - WAF, Anti-DDoS and network protection - Data security and compliance best practices - Resource Access Management (RAM) and identity control |
| 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% | - Multi-zone deployment and disaster recovery - Auto Scaling and Server Load Balancer (SLB) - Performance optimization and resilience design |
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NEW QUESTION # 26
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,C
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 # 27
Keanu uses Elastic Compute Service (ECS) instances to run batch jobs to process data at 00:00 AM and 04:00 AM every day. An Auto Scaling group manages the ECS instances, which run as pay-as-you-go instances. If a batch job fails on one instance, another instance will reprocess the job.
Which of the following solutions provides the MOST cost-effective way to run these jobs?
Answer: B
Explanation:
The workload is an excellent candidate for preemptible, currently called spot, ECS capacity because it is short- lived, batch-oriented, horizontally scalable, and explicitly tolerant of interruption. The question states that if an instance fails, another instance can reprocess the job. That removes the main architectural objection to using interruptible compute.
Alibaba Cloud describes ECS spot instances as unused ECS capacity offered at substantially reduced prices compared with standard pay-as-you-go instances. The documented recommended scenarios include short- term tasks, data analytics, and applications that are highly fault tolerant and able to tolerate interruption.
Savings Plans and reserved-instance-style commitments can reduce costs for predictable long-term baseline consumption, but this workload runs only at two scheduled times each day. Purchasing committed capacity for a sporadic workload can leave paid capacity economically underutilized. Higher-specification pay-as-you- go instances also do not inherently minimize compute cost.
Using a launch template with preemptible/spot ECS instances allows Auto Scaling to add inexpensive compute when the batch workload requires it, while the application ' s retry mechanism handles interruptions.
Study Guide reference: Core Compute Infrastructure Deep Dive; Scalable Architecture on Alibaba Cloud; cost optimization for elastic compute.
NEW QUESTION # 28
A fintech startup is building a transaction monitoring application using Alibaba Cloud Container Service for Kubernetes (ACK). They are debating between adopting a dedicated cluster setup versus a serverless architecture to ensure scalability and cost efficiency.
What factors should they consider to decide between a serverless or a dedicated ACK deployment? (Correct answers: 2)
Answer: A,D
Explanation:
The fundamental decision is the trade-off between infrastructure control and infrastructure abstraction.
A conventional managed/dedicated Kubernetes environment gives architects greater control over node pools, instance types, resource placement, operating-system configuration, network topology, specialized hardware, and related infrastructure choices. This makes Option B appropriate when a regulated fintech application requires exact control over the compute and networking environment.
ACK Serverless takes the opposite approach. Alibaba Cloud manages the underlying node infrastructure, and workloads run without customers purchasing or maintaining worker nodes. Alibaba ' s documentation states that ACK Serverless uses pay-as-you-go resource consumption and can scale pods rapidly, making it particularly appropriate for variable or unpredictable workloads.
Alibaba Cloud also explicitly describes ACK Serverless as Kubernetes-compatible infrastructure where customers focus on application development while paying according to CPU and memory resources requested by workloads.
Option A is too absolute: dedicated Kubernetes infrastructure does not inherently guarantee lower application latency; latency depends on workload, topology, instance configuration, networking, and scaling behavior.
Option C is also incorrect because both managed and serverless ACK environments integrate deeply with Alibaba Cloud networking services.
Study Guide reference: Core Infrastructure Deep Dive - ACK deployment models, serverless containers, cluster control, elasticity, and cost optimization.
NEW QUESTION # 29
A company is designing an application. The application uses a Function Compute function to receive information through API Gateway and to store the information in an ApsaraDB RDS for PostgreSQL database.
During the proof-of-concept stage, the company noticed that the volume and velocity of data is very high, and they had to increase the Function Compute quotas significantly. Even with the increased quotas, they would sometimes lose data. The company has hired Javert, a Cloud Architect, to recommend a new design to improve scalability and minimize the configuration effort.
Which of the following solutions will meet these requirements?
Answer: B
Explanation:
The application needs asynchronous decoupling between high-volume ingestion and database writes. Simple Message Queue provides exactly this load-leveling pattern. The first Function Compute function can acknowledge incoming requests quickly and place each item into SMQ. A second function consumes the queued messages and writes them to RDS at a rate the database can sustain.
SMQ is a distributed managed messaging service specifically designed to decouple producers and consumers.
Alibaba Cloud identifies traffic-spike buffering, asynchronous processing, and independent component scaling as core SMQ use cases. Queue messages are delivered at least once during their validity period, significantly improving durability compared with directly coupling every incoming API request to a database write.
Function Compute also integrates directly with SMQ queues and topics as event sources, reducing custom integration and infrastructure management.
Kafka could also provide durable asynchronous messaging, but it is a more elaborate streaming platform than necessary for this workload and therefore carries additional configuration complexity. Moving to ECS reintroduces server administration, while replacing RDS does not solve the ingestion decoupling problem.
Study Guide reference: Core Infrastructure Deep Dive - Function Compute, messaging architectures, asynchronous processing, and scalable application integration.
NEW QUESTION # 30
FirstBuy is a rapidly growing global e-commerce company. The company hosts its web application on Alibaba Cloud. The application serves both static and dynamic content. Transactional data is stored in an ApsaraDB RDS database.
Lately, customers have been complaining about slow page loading speeds.
As a Cloud Architect, which combination of actions can you take to solve this problem? (Correct answers: 2)
Answer: B,D
Explanation:
Alibaba Cloud CDN addresses the front-end performance problem by caching static resources at edge Points of Presence close to users. Instead of every image, JavaScript file, stylesheet, or other cacheable object being retrieved from the origin application, CDN returns cached copies from nearby edge nodes. This reduces network latency and decreases load on the original application servers. Alibaba Cloud specifically identifies reduced latency and lower origin workload as primary benefits of CDN.
Read/write splitting addresses database-side pressure. ApsaraDB RDS for MySQL can use database proxy functionality to direct write operations to the primary instance and distribute eligible read operations to read- only instances. This is especially effective for read-heavy web applications because it reduces CPU and I/O load on the primary database and increases aggregate read throughput.
A multi-zone database improves availability rather than directly solving page-load latency. AnalyticDB is an analytical database and should not replace RDS simply to handle transactional data. OSS is optimized for static objects; hosting dynamic application content there does not fit its object-storage model.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - CDN acceleration, RDS read scaling, database proxies, and application performance optimization.
NEW QUESTION # 31
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