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| Section | Objectives |
|---|---|
| Topic 1: Building Enterprise-grade Networks on Alibaba Cloud | - Cloud Networking Deep Dive |
| Topic 2: Securing Workloads on Alibaba Cloud | - Alibaba Cloud Security Deep Dive |
| Topic 3: Delivering Services and Content on Alibaba Cloud | - Delivering Services and Content on Alibaba Cloud |
| Topic 4: Core Infrastructure Deep Dive | - Core Compute Infrastructure Deep Dive - Core Storage Infrastructure Deep Dive - Best Practices for Database Services |
| Topic 5: Building Highly Available, Performant Cloud Architecture | - Leveling up Your Core Infrastructure |
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NEW QUESTION # 47
Jason ' s company is running a traditional web application on Alibaba Cloud ECS instances. The company wants to improve efficiency by refactoring the application as microservices that run on containers. The application must be deployed across multiple environments: production and testing. Load for the application is variable, but the minimum load and maximum load are known. As a Cloud Architect, you have been assigned the job of designing the updated application with a serverless architecture that minimizes operational complexity.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
Serverless App Engine is the strongest fit because it provides an application-oriented serverless platform specifically intended for hosting containerized and microservice applications without requiring the organization to administer Kubernetes clusters or ECS infrastructure. SAE can deploy applications directly from container images, including images stored in Alibaba Cloud Container Registry, and automatically manages provisioning, application lifecycle, scaling, monitoring, and underlying resources.
SAE namespaces also provide logical isolation for development, testing, and production environments. This directly addresses the requirement for separate production and testing deployments while avoiding the overhead of maintaining separate ACK clusters.
SAE integrates with Application Load Balancer for Layer 7 ingress and routing. ALB can expose SAE applications and route traffic to the appropriate backend application instances.
ACK Serverless removes worker-node management but still introduces Kubernetes objects and cluster administration. ACK Managed requires even greater cluster administration. Function Compute is optimized for functions and event-driven processing rather than straightforward migration of a multi-service container application.
Study Guide reference: Core Infrastructure Deep Dive - serverless compute, SAE, containerized applications, microservices, and application scaling.
NEW QUESTION # 48
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: D
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 # 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: A,B,C
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
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,C,D
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 # 51
A company has a data ingestion workflow that consists of:
* An Alibaba Cloud Simple Message Queue topic for notifications about new data deliveries.
* An Alibaba Cloud Function Compute service to process the data and record metadata.
The company observes that the ingestion workflow fails occasionally because of network connectivity issues.
When such a failure occurs, the Function Compute instance does not ingest the corresponding data unless the company manually reruns the job.
Which combination of actions should a solutions architect take to ensure that the Function Compute instance ingests all data in the future? (Correct answers: 2)
Answer: C,E
Explanation:
The core problem is durability during transient processing or network failures. A topic provides publish
/subscribe notification delivery, but placing an SMQ queue behind the topic creates a persistent buffer between the notification producer and the Function Compute consumer. Alibaba Cloud supports subscribing a queue to an SMQ topic so that every message published to the topic is copied into the queue and retained for consumption.
Function Compute should then process records from the queue. Queue semantics provide the required failure recovery: after a consumer receives a message, it becomes temporarily invisible. The consumer deletes the message only after successful processing. If processing fails-for example because of a temporary connectivity problem-the message is not deleted and becomes visible again after the visibility timeout, allowing another processing attempt.
Function Compute also supports SMQ queue triggers with configurable retries, fault-tolerance behavior, and dead-letter handling, which can further strengthen this architecture.
Increasing memory does not fix network reliability, and multi-region deployment alone does not guarantee message persistence. A durable queue therefore prevents transient failures from silently losing ingestion work.
Study Guide reference: Core Infrastructure Deep Dive - Simple Message Queue, Function Compute, durable asynchronous processing, retries, and decoupled event-driven architecture.
NEW QUESTION # 52
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