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| Section | Weight | Objectives |
|---|---|---|
| Core Infrastructure Deep Dive | 26% | - Core Storage Infrastructure Deep Dive
|
| Securing Workloads on Alibaba Cloud | 22% | - Alibaba Cloud Security Deep Dive
|
| Building Enterprise-grade Networks on Alibaba Cloud | 18% | - Cloud Networking Deep Dive
|
| Building Highly Available, Performant Cloud Architecture | 22% | - Leveling up Your Core Infrastructure
|
| Delivering Services and Content on Alibaba Cloud | 12% | - Delivering Services and Content on Alibaba Cloud
|
>> Alibaba Cloud CAP-C01基礎問題集 <<
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質問 # 10
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?
正解：C
解説：
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.
質問 # 11
A company runs its cloud infrastructure on Alibaba Cloud. Jose, a Cloud Architect for the company, needs to define the infrastructure as code. Currently, the infrastructure is deployed in a single Alibaba Cloud region.
The company ' s business expansion plan includes deployments across multiple regions and multiple Alibaba Cloud accounts.
What should Jose do to meet these requirements?
正解：D
解説：
ROS Stack Groups are specifically designed for centrally deploying a common Resource Orchestration Service template across multiple Alibaba Cloud accounts and regions. This makes option D the architecture that directly addresses both dimensions of the requirement: multi-account governance and multi-region infrastructure-as-code deployment.
Alibaba Cloud documentation states that stack instances belonging to a stack group can deploy stacks into destination accounts and different regions. With Resource Directory integration and service-managed permissions, trusted access can be enabled so that stack groups are operated by a management account or delegated administrator while deployments occur in organization member accounts.
Nested stacks are primarily a template-composition mechanism; they do not constitute centralized multi- account deployment orchestration. Manually combining ordinary ROS templates and RAM policies could technically enable cross-account operations, but it recreates functionality that Stack Groups provide natively and increases operational overhead. Resource Directory and Cloud Governance alone also do not replace the actual multi-account, multi-region stack deployment mechanism.
Therefore, Resource Directory establishes the organizational account structure while ROS Stack Groups provide repeatable infrastructure deployment across those accounts and target regions.
Study Guide reference: Core Infrastructure Deep Dive; Resource Orchestration Service, multi-account governance, and repeatable cloud architecture.
質問 # 12
A global hotel chain is migrating its reservation system to Alibaba Cloud. The system must provide high availability across multiple regions, ensure data consistency, and support real-time updates to prevent double bookings. Additionally, the system should integrate with third-party payment gateways securely.
Which combination of Alibaba Cloud services should the hotel chain implement to architect this reservation system effectively? (Correct answers: 3)
正解：A、C、D
解説：
Option A refers to PolarDB Global Database Network (GDN), Alibaba Cloud ' s architecture for connecting PolarDB clusters across regions. A GDN contains a primary cluster and secondary regional clusters. Data changes are continuously replicated, local reads can be served by regional clusters, and write operations are routed to the primary cluster. Maintaining a single authoritative write path is particularly important for reservation transactions where concurrent booking operations must be controlled consistently.
Option C provides resilient application capacity by deploying ECS application servers in multiple regions and using ALB within each regional deployment to distribute requests among healthy instances. In a complete production implementation, a global routing mechanism such as DNS traffic management or Global Accelerator would normally direct users between those regional application stacks.
Function Compute is suitable for payment-gateway integrations because payment callbacks and API interactions are event-driven or request-driven workloads. Function Compute can be exposed securely through API Gateway, which provides routing, authentication, traffic control, and API management without persistent integration servers.
OSS versioning is useful for protecting stored objects but does not supply the transactional database, regional application availability, or payment-processing functionality required here.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - multi-region PolarDB, GDN, regional application tiers, ALB, and serverless integration.
質問 # 13
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)
正解：C、D、E
解説：
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.
質問 # 14
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)
正解：A、D
解説：
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.
質問 # 15
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