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| Section | Objectives |
|---|---|
| Building Highly Available, Performant Cloud Architecture | - Leveling up Your Core Infrastructure |
| Building Enterprise-grade Networks on Alibaba Cloud | - Cloud Networking Deep Dive |
| 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 |
| Securing Workloads on Alibaba Cloud | - Alibaba Cloud Security Deep Dive |
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NEW QUESTION # 49
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)
Answer: B,C,D
Explanation:
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.
NEW QUESTION # 50
An online education company plans to migrate their services to Alibaba Cloud. They want to improve their video streaming quality during peak hours using Alibaba Cloud services. As a Cloud Architect, you are tasked with building a solution to manage traffic spikes without compromising on video quality.
What combination of services would BEST fit their requirements? (Correct answers: 3)
Answer: A,C,D
Explanation:
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.
NEW QUESTION # 51
A worldwide retail company is adapting their e-commerce platform to handle spikes in online traffic during annual sales events. They are using Alibaba Cloud services and need a solution that can automatically scale resources to maintain performance during these peaks.
What solutions should the company implement to efficiently manage their resources during high-traffic periods? (Correct answers: 3)
Answer: A,B,C
Explanation:
The strongest architecture for an existing e-commerce platform experiencing predictable but substantial traffic spikes is OSS/CDN + ALB + Auto Scaling. Auto Scaling is specifically designed to increase ECS capacity when traffic or resource utilization rises and remove unnecessary instances after demand subsides. Alibaba Cloud documents this as a core mechanism for handling traffic spikes while avoiding permanent over- provisioning.
ALB complements Auto Scaling by distributing requests across healthy ECS backend instances. As scaling creates additional instances, they can be automatically added to the ALB server group, allowing traffic to use the enlarged compute pool immediately.
Static images and videos should be moved to OSS and accelerated through CDN. CDN caches static resources on geographically distributed edge nodes, substantially reducing requests reaching the ECS application tier and lowering origin bandwidth and processing load during sales events.
Function Compute is highly elastic, but introducing it merely to supplement an existing server-based application normally requires application restructuring. It is therefore less directly applicable than the three mechanisms specifically designed to scale and offload the existing architecture.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - Auto Scaling, ALB, CDN offloading, and elastic ECS architecture.
NEW QUESTION # 52
Clash for Victory is a popular online game that is hosted on Alibaba Cloud. As latency can have a huge impact on gameplay, the developers for the game want to implement a mechanism to route all requests to the closest access point, monitor the health of the game, and redirect traffic to healthy backend servers.
The game is deployed in multiple regions on Elastic Compute Service (ECS) instances that are part of Auto Scaling groups configured behind Application Load Balancers (ALBs).
As a Cloud Architect, what course of action would you take?
Answer: C
Explanation:
Global Accelerator is designed for precisely this class of global interactive application. Clients connect through Alibaba Cloud ' s nearby acceleration access points, after which traffic traverses Alibaba Cloud ' s optimized global network toward the most appropriate backend region. This reduces Internet routing variability and improves end-to-end latency for latency-sensitive workloads such as gaming.
GA listeners define the application protocol and ports, while endpoint groups represent backend deployments in different regions. With intelligent routing, GA can select a nearby healthy endpoint group and forward requests accordingly. Alibaba Cloud also provides endpoint health checks; when an endpoint becomes unhealthy, new requests can automatically be directed to healthy endpoints.
CDN is highly effective for static cacheable content but is not the correct primary mechanism for arbitrary real-time game traffic and regional backend health-based routing. RDS and Tair improve application data performance but do not solve global network acceleration.
Therefore, GA in front of regional ALBs provides the correct architecture: nearest-entry acceleration, Alibaba Cloud backbone transport, regional endpoint groups, health monitoring, and failover.
Study Guide reference: Delivering Services and Content on Alibaba Cloud - Global Accelerator, ALB, multi-region traffic management, and high availability.
NEW QUESTION # 53
Paul runs a successful online game. The game is deployed as a three-tier architecture in a VPC with ApsaraDB RDS for MySQL at the database layer. The game allows players to compete in real-time, and Paul wants to display a top-10 scoreboard in near-real time.
As a Cloud Architect, which of the following solutions would you propose for Paul ' s use case?
Answer: B
Explanation:
Tair is the appropriate architecture for a frequently changing game leaderboard because Redis-compatible sorted data structures are optimized for high-throughput score updates and ranking operations. Instead of repeatedly executing expensive aggregation and sorting queries against the relational database, the application can maintain player scores in Tair and retrieve the highest-ranked members directly.
Alibaba Cloud specifically documents leaderboard implementations based on Tair ' s sorted-set capabilities.
Tair ' s enhanced exZset functionality supports scalable ranking use cases and can distribute large leaderboards across multiple internal structures while preserving rank calculation.
This also protects RDS from high-frequency scoreboard queries. RDS remains the durable transactional database, while Tair operates as the low-latency serving and ranking layer for frequently accessed score data.
CDN caching is unsuitable because the leaderboard changes continually and requires near-real-time freshness.
A second relational PostgreSQL database would duplicate database infrastructure without providing the specialized in-memory ranking semantics required. An RDS read replica can reduce pressure on the primary database, but calculating and sorting rankings through SQL for every refresh remains less efficient than maintaining rankings directly in an in-memory sorted structure.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - Tair, caching, low- latency architectures, and database performance optimization.
NEW QUESTION # 54
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