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| Section | Weight | Objectives |
|---|---|---|
| Delivering Services and Content on Alibaba Cloud | 12% | - Service integration and access optimization - CDN and content delivery strategies - Alibaba Cloud DNS and domain management |
| Securing Workloads on Alibaba Cloud | 22% | - Resource Access Management (RAM) and identity control - Data security and compliance best practices - WAF, Anti-DDoS and network protection |
| Building Enterprise-grade Networks on Alibaba Cloud | 18% | - VPC design and configuration - Express Connect and VPN Gateway - Network routing and security groups |
| Core Infrastructure Deep Dive | 26% | - Elastic Compute Service (ECS) and serverless computing - Object Storage Service (OSS) and storage solutions - File Storage NAS and block storage |
| Building Highly Available, Performant Cloud Architecture | 22% | - Multi-zone deployment and disaster recovery - Auto Scaling and Server Load Balancer (SLB) - Performance optimization and resilience design |
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NEW QUESTION # 16
Kenneth plans to deploy a real-time data processing platform on Alibaba Cloud to support his company ' s Internet-of-Things (IoT) business. The platform will handle millions of sensor data points collected from smart devices globally. Kenneth needs to ensure high throughput and low latency for data ingestion and analysis.
Which solutions should Kenneth consider integrating into the platform architecture to meet his expected performance requirements? (Correct answers: 3)
Answer: A,B,D
Explanation:
Time Series Database is specifically designed for timestamped telemetry generated by IoT devices, monitoring systems, and industrial equipment. Alibaba Cloud identifies IoT device monitoring as a core TSDB scenario, including continuously collecting, storing, analyzing, and querying device-status and business-event data in real time.
ApsaraMQ for Kafka provides the high-throughput ingestion and buffering layer. It is a managed distributed Kafka platform intended for real-time data pipelines, monitoring-data aggregation, log collection, streaming processing, and large-scale analytics. Its partitioned distributed architecture lets producers ingest very large event volumes while downstream consumers process the streams independently.
Function Compute supplies elastic event processing without persistent compute servers. Alibaba Cloud supports ApsaraMQ for Kafka triggers that invoke Function Compute automatically whenever new records arrive in Kafka topics. This enables functions to perform transformations, filtering, enrichment, alert generation, or routing in near real time.
EMR is valuable for Hadoop/Spark-style batch analytics, but the question specifically emphasizes low-latency real-time ingestion and analysis. It therefore does not replace the streaming components selected above.
Study Guide reference: Core Infrastructure Deep Dive - IoT telemetry, TSDB, ApsaraMQ for Kafka, Function Compute, and real-time streaming architectures.
NEW QUESTION # 17
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: D
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 # 18
Muthu is a Cloud Architect who is designing the architecture of a new application being deployed to Alibaba Cloud. The application will run on Elastic Compute Service (ECS) pay-as-you-go instances and will automatically scale across multiple zones based on load. The ECS cluster will scale in and out frequently throughout the day. An Application Load Balancer (ALB) will handle the load distribution. The architecture needs to support distributed session data management.
What should Muthu do to ensure that the architecture supports distributed session data management?
Answer: B
Explanation:
Tair (Redis OSS-Compatible) is the appropriate centralized session-state layer for an elastic, distributed application. When ECS instances frequently scale in and out, application sessions must not depend on the lifecycle of any individual compute node. By storing session tokens and session state in Tair, every ECS instance can retrieve the same session information, making the web/application tier effectively stateless and compatible with Auto Scaling.
Alibaba Cloud Tair is a fully managed Redis-compatible database designed for high throughput, low latency, elastic scalability, and high availability. Alibaba Cloud also documents session-oriented Tair use cases in which session tokens are stored as Redis/Tair data structures with TTL values, demonstrating precisely why the service fits distributed session management.
Session stickiness is weaker because it attempts to keep a user on a specific backend and therefore creates affinity to an instance that might be removed during scaling or failover. CloudMonitor is an observability service, not a session store. STS issues temporary Alibaba Cloud credentials and does not maintain application user sessions.
Consequently, separating session state from ECS compute by storing it in Tair gives the architecture the elasticity and fault tolerance required.
Study Guide reference: Distributed Architecture on Alibaba Cloud; Performant Architecture; Core Infrastructure Deep Dive.
NEW QUESTION # 19
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)
Answer: B,C
Explanation:
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.
NEW QUESTION # 20
Liam ' s company operates an on-premises data analytics platform across 12 servers. The system runs scheduled jobs (70% of the time, each job taking anywhere from 20 minutes to 2 hours to complete, with tight SLAs) and user jobs (30% of the time, each job taking less than 5 minutes, with no SLA requirements).
Scheduled jobs must continue to meet SLAs during system failures, while user jobs can be delayed.
As a Cloud Architect, you are tasked to migrate the system to Alibaba Cloud ECS using a consumption-based model while maintaining the same high availability and SLA requirements.
Which solution is the MOST cost-effective and flexible?
Answer: A
Explanation:
Option A offers the strongest combination of resilience, predictable baseline capacity, flexibility, and reduced cost. Nine standard pay-as-you-go ECS instances are spread evenly across three zones and can support the scheduled workloads that have strict SLA requirements. The remaining three instances use lower-cost preemptible/spot capacity for user jobs that can tolerate interruption or delay.
Alibaba Cloud describes spot instances as offering the same compute performance as ordinary pay-as-you-go ECS but at substantially lower prices, with the trade-off that capacity can be reclaimed. This makes spot capacity appropriate for interruption-tolerant workloads but inappropriate as the sole capacity supporting strict SLAs.
A Savings Plan discounts eligible pay-as-you-go ECS usage while preserving significantly greater flexibility than traditional reserved-instance matching. Current Alibaba Cloud documentation states that Savings Plans apply discounts to pay-as-you-go usage and can offer flexibility across instance configurations; they do involve a one- or three-year hourly spend commitment, but they do not provision or reserve fixed ECS capacity.
Options C and D concentrate too much reliable capacity into fewer zones or place excessive workload on interruptible capacity. Option A therefore provides the best workload-tiering and multi-zone design among the available choices.
Study Guide reference: Building Highly Available, Performant Cloud Architecture - multi-zone ECS, workload resilience, Savings Plans, and spot instances.
NEW QUESTION # 21
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