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| Section | Objectives |
|---|---|
| Delivering Services and Content on Alibaba Cloud | - Delivering Services and Content on Alibaba Cloud |
| Core Infrastructure Deep Dive | - Core Compute Infrastructure Deep Dive - Best Practices for Database Services - Core Storage Infrastructure Deep Dive |
| Building Highly Available, Performant Cloud Architecture | - Leveling up Your Core Infrastructure |
| Building Enterprise-grade Networks on Alibaba Cloud | - Cloud Networking Deep Dive |
| Securing Workloads on Alibaba Cloud | - Alibaba Cloud Security Deep Dive |
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27. Frage
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?
Antwort: C
Begründung:
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.
28. Frage
A retail chain is utilizing Alibaba Cloud ' s PolarDB to manage its inventory and sales databases. They need to ensure that their SQL queries have minimal impact on their production workload while enabling data analysts to run complex analytical queries.
How can the retail chain optimize their database operations for both transactional and analytical workloads?
(Correct answers: 2)
Antwort: B,D
Begründung:
Option B reduces interference between transaction processing and read-heavy workloads inside PolarDB.
PolarDB ' s read/write splitting automatically routes writes to the primary node while distributing eligible reads across read-only nodes. This allows reporting and ordinary SELECT traffic to consume separate compute capacity instead of competing with critical transactional writes on the primary node. Alibaba Cloud specifically identifies reporting, analytics, and read-heavy applications as appropriate read/write-splitting scenarios.
Option D provides stronger isolation for computationally expensive analytics. DTS can continuously synchronize PolarDB for MySQL data into MaxCompute using initial full synchronization followed by ongoing incremental change capture. Analysts can then execute large-scale analytical workloads against MaxCompute without placing those queries on the production transactional database.
Running queries on ECS does not inherently remove load from PolarDB because the SQL operations would still execute against the database. Introducing MongoDB merely to execute analytical queries creates a second database technology without an appropriate replication or analytical architecture.
Together, PolarDB read/write splitting optimizes operational read traffic while DTS-to-MaxCompute isolates heavyweight analytical processing, producing an effective HTAP-style separation of concerns.
Study Guide reference: Core Infrastructure Deep Dive - PolarDB, read/write splitting, DTS, MaxCompute, and analytical workload offloading.
29. Frage
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?
Antwort: C
Begründung:
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.
30. Frage
A company is building a big data analytics platform on Alibaba Cloud to process and analyze petabytes of data. They require a highly scalable storage solution that can handle both structured and unstructured data, and a processing framework that supports batch and interactive queries.
Which combination of Alibaba Cloud services should the company use to build this platform? (Correct answers: 3)
Antwort: B,C,D
Begründung:
OSS, E-MapReduce, and MaxCompute collectively provide the strongest large-scale analytics architecture among the listed services.
OSS supplies the scalable data-lake storage layer. Alibaba Cloud describes OSS-based data lakes as capable of retaining structured, semi-structured, and unstructured datasets at very large scale while decoupling storage capacity from compute resources. OSS storage can scale independently into exabyte-scale environments.
E-MapReduce provides managed big-data processing based on ecosystems such as Hadoop and Spark and integrates directly with OSS. EMR DataLake configurations support Spark and Hive for batch workloads and Presto/Trino for interactive SQL analysis, making EMR highly appropriate for workloads requiring both scheduled processing and ad-hoc querying.
MaxCompute supplies a massively scalable managed data-warehouse and analytical processing layer. Alibaba Cloud also supports architectures where MaxCompute processes structured analytical datasets while OSS provides the underlying flexible data-lake storage.
PolarDB is an operational relational database and is not intended to act as the primary petabyte-scale data-lake processing platform. Elasticsearch is valuable for search and log analytics but does not replace the combination of OSS, EMR, and MaxCompute for generalized big-data processing.
Study Guide reference: Core Infrastructure Deep Dive - OSS data lakes, EMR, MaxCompute, distributed analytics, batch processing, and interactive query architectures.
31. Frage
Camila has set up a simple blog hosted on a single Elastic Compute Service (ECS) instance, storing images and files on an Elastic Block Storage (EBS) disk within the ECS instance, and relying on an ApsaraDB RDS for MySQL instance for its data tier. Camila wants to scale up her blog and enhance both resilience and performance with minimal changes to the application.
As a Cloud Architect, which combination of actions can you take to improve both resilience and performance of the website? (Correct answers: 3)
Antwort: B,D,E
Begründung:
The existing architecture has two important bottlenecks: a single ECS compute node and files stored on storage attached to that individual ECS instance. Option E eliminates the compute single point of failure. A custom image allows identical ECS instances to be created, while Auto Scaling maintains multiple instances and ALB distributes requests across healthy backends. This provides horizontal scalability and significantly stronger application availability.
Option D addresses shared application storage. File Storage NAS is a managed, POSIX-compatible shared filesystem that multiple ECS instances can mount concurrently. All web servers therefore see the same files, while NAS handles storage scalability and consistency independently from the ECS lifecycle.
Option A improves performance by moving cacheable website content toward users. CDN serves static resources from nearby POPs and reduces requests and bandwidth reaching the origin infrastructure.
Although OSS is excellent for native object-storage workloads, mounting OSS through ossfs does not provide the same filesystem semantics and consistency characteristics as NAS for a traditional application requiring minimal modification. Global Accelerator primarily optimizes network paths and does not eliminate the application ' s compute or storage single points of failure.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - ALB, Auto Scaling, NAS shared storage, and CDN.
32. Frage
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