Alibaba Cloud CAP-C01 Dumps Questions Answers

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.

==================================================

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.

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.

==================================================