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| Section | Objectives |
|---|---|
| Core Infrastructure Deep Dive | - Best Practices for Database Services - Core Compute Infrastructure Deep Dive - Core Storage Infrastructure Deep Dive |
| 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 |
| Building Highly Available, Performant Cloud Architecture | - Leveling up Your Core Infrastructure |
| Securing Workloads on Alibaba Cloud | - Alibaba Cloud Security Deep Dive |
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NEW QUESTION # 22
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: C
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 # 23
Jenny developed a healthcare analytics platform on Alibaba Cloud. To meet performance, disaster recovery, and compliance requirements, the platform requires continuous data replication across zones for fault tolerance and cost-efficient storage of historical patient data for regulatory retention. Jenny has recruited you to help build a solution to address these challenges.
Which of the following strategies would you propose? (Correct answers: 2)
Answer: B,C
Explanation:
Data Transmission Service and OSS lifecycle management address the two distinct requirements:
continuously replicated operational data and economical long-term retention.
DTS provides real-time data synchronization by continuously capturing changes from a source database and applying them to a destination. Alibaba Cloud identifies active geo-redundancy, disaster recovery, cross- region replication, and real-time data synchronization as standard DTS scenarios. Maintaining a secondary synchronized dataset in another failure domain therefore provides substantially better recovery characteristics than relying only on periodic backups.
Historical data has a different access pattern. Regulatory medical records can remain necessary for years while being rarely retrieved. OSS lifecycle policies can automatically transition aging objects from frequently accessed storage into lower-cost storage classes such as Archive. Alibaba Cloud specifically describes lifecycle policies for medical records and other compliance datasets that become cold over time.
Option B provides backup protection but does not directly provide the requested continuous replication architecture and unnecessarily introduces self-managed databases on ECS. DMS simplifies database administration but is not a data-replication or archival-storage mechanism.
Consequently, DTS supplies the live replication/fault-tolerance layer, while OSS lifecycle policies supply automated and cost-efficient long-term retention.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - DTS replication, disaster recovery, OSS lifecycle management, and compliance-oriented storage.
NEW QUESTION # 24
An educational technology company is migrating its learning management system (LMS) to Alibaba Cloud.
The company mainly serves enterprise-grade customers, hence their platform must support automated backups, high availability across multiple zones, and granular access control for sensitive student records.
You have been hired as the lead Cloud Architect to build their architecture on Alibaba Cloud.
Which of the following architectural components will fit their requirements? (Correct answers: 3)
Answer: B,C,D
Explanation:
Each selected option maps directly to one of the stated enterprise requirements.
A multi-zone PolarDB deployment protects the database against availability-zone-level infrastructure failure.
Alibaba Cloud ' s multi-AZ PolarDB architecture keeps replicated data in separate zones and can use standby resources for failover when the primary zone becomes unavailable, resulting in a higher database SLA than a single-zone deployment.
Cloud Backup provides centralized policy-driven backup and recovery for Alibaba Cloud and on-premises resources, including ECS data, databases, NAS, OSS, and other supported workloads. This addresses the requirement for automated application and database protection without building custom backup infrastructure.
RAM roles and associated policies provide fine-grained authorization. RAM policies define allowed actions, affected resources, and optional access conditions, enabling least-privilege access to sensitive student data and administrative services.
Leaving OSS data unencrypted conflicts with the handling of sensitive enterprise information, while manual ACK scaling does not contribute to the stated requirement for high availability or automated operations.
The resulting architecture therefore combines multi-AZ database resilience, managed backup protection, and fine-grained identity authorization.
Study Guide reference: Securing Workloads on Alibaba Cloud - RAM; Building Highly Available, Performant Cloud Architecture - PolarDB HA and Cloud Backup.
NEW QUESTION # 25
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 # 26
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: A,B,E
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 # 27
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