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| Section | Objectives |
|---|---|
| Topic 1: Cloud-based Architecture Design of Conventional Applications | - Compute service planning and Elastic Cloud Server (ECS) - Cloud security service planning and design - Storage and Object Storage Service (OBS) - Database service planning and migration - Network design and Virtual Private Cloud (VPC) |
| Topic 2: Huawei Cloud Innovations and Solutions | - Innovative services overview (EI, IoT, data solutions) - Emerging architecture solution practices |
| Topic 3: Enterprise Application Architectures and Cloud Technologies | - Cloud technology concepts and Huawei Cloud overview - Enterprise application architectures fundamentals |
| Topic 4: Cloud Native Application Architecture Design | - Huawei cloud native services and solutions - Container and serverless design patterns - Cloud native technologies and microservices design |
| Topic 5: Huawei Cloud O&M Solution | - Monitoring, logging, and operational best practices - O&M strategies and Huawei Cloud operations tools |
>> Certification H13-821_V3.5 Torrent <<
Almost everyone is trying to get HCIP-Cloud Service Solutions Architect V3.5 (H13-821_V3.5) certification to update their CV or get the desired job. Nowadays, everyone is interested in taking the HCIP-Cloud Service Solutions Architect V3.5 (H13-821_V3.5) exam because it has multiple benefits for the future. Every candidate faces just one problem, and that is not getting updated HCIP-Cloud Service Solutions Architect V3.5 (H13-821_V3.5) practice questions.
NEW QUESTION # 42
For processing high-quality images and videos, as well as performing big data analytics, memory-optimized Elastic Cloud Servers (ECSs) are recommended, including M8, M7, and M5 ECSs.
Answer: A
Explanation:
"Memory-optimized ECSs are designed for large in-memory datasets and high-speed network scenarios."
[Related content] GPU-accelerated ECSs support graphics processing, rendering, deep learning and parallel computation. Different workload bottlenecks require different ECS families.
Huawei source: Training PDF pp. 62-63
Technical Deep Dive
Memory-optimized ECSs are designed for workloads whose dominant requirement is a large memory working set and high-speed access to in-memory datasets. Appropriate examples include databases, in- memory caches, some MapReduce or Hadoop stages and memory-intensive analytical applications. That does not make them the universal recommendation for high-quality image processing, video processing and big data workloads.
Image rendering, media transcoding, certain graphics workloads and AI-assisted processing may depend primarily on GPU acceleration or specialized parallel computing hardware. Huawei Cloud provides GPU- accelerated ECSs for these classes of workloads. Compute-intensive analytics may instead require a CPU- optimized flavor; storage-intensive pipelines may be constrained by disk throughput or network bandwidth.
Resource selection must follow workload profiling rather than a single generic designation.
The question also names M8, M7 and M5 without establishing the complete family list in the cited V3.5 course. The training identifies the M7 memory-optimized line but does not support the blanket statement across all three labels. Consequently B, FALSE, is the defensible curriculum answer. This question is somewhat broad: a memory-intensive image or video application could legitimately benefit from an M-series ECS. A more technically precise examination question would specify GPU-dependent rendering or video processing and clearly distinguish that workload from in-memory big data analytics. Architects should benchmark CPU, RAM, GPU, storage and networking before final selection.
NEW QUESTION # 43
After detecting that a container has crashed, Kubernetes can restart it. Which of the following probe types does Kubernetes use to check the container's response and determine whether it should be restarted?
Answer: B
Explanation:
"Kubernetes offers three types of health check probes for checking pod status: liveness, readiness, and startup probes."
"A liveness probe checks whether a container is running ... If the liveness probe of a container fails, the cluster restarts the container."
"A readiness probe determines when a container is ready to accept traffic." Huawei source: Training PDF p. 419 Technical Deep Dive Kubernetes defines three standard application health probes: liveness, readiness and startup. Option B , liveness, is the correct probe for checking whether a running application is healthy enough to continue. A repeatedly failing liveness probe causes Kubernetes to restart the affected container according to its configured behavior.
For example, a web process may still exist but become deadlocked or stop responding to HTTP requests. An HTTP liveness probe targeting /health can detect the failure even though the process has not terminated.
Typical configuration includes httpGet, initialDelaySeconds, periodSeconds and failureThreshold. A startup probe, option C, is used when an application needs additional initialization time; it prevents liveness or readiness checks from incorrectly marking the workload as failed before startup completes. A readiness probe, option D, determines whether the pod should receive Service traffic. Failed readiness normally removes the endpoint from the eligible pool rather than restarting the container. 'Monitoring probe', option A, is not a standard Kubernetes health probe name.
A precise distinction is that an actual container process crash can also cause an automatic restart based on the pod's restartPolicy even without a liveness probe. The question conflates a crash with liveness detection, but it asks which probe drives restart for an unresponsive workload; this is liveness. Architects should tune probes carefully to avoid crash loops, unnecessary restarts or routing traffic to applications that are not yet ready.
Answer B matches the Huawei health-check probe explanation.
Technical commands / calculations from the reviewed batch
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 10
periodSeconds: 5
failureThreshold: 3
NEW QUESTION # 44
When you create a cluster in Cloud Container Engine (CCE), which of the following container network models can you select?
Select all that apply.
Answer: B,D
Explanation:
"CCE supports the following container network models: container tunnel network, VPC network, and Cloud Native Network 2.0."
"The container tunnel network is constructed on but independent of the node network through tunnel encapsulation. This network model uses VXLAN to encapsulate Ethernet packets into UDP packets and transmits them in tunnels. Open vSwitch serves as the backend virtual switch." Huawei source: Training PDF pp. 456-457 Technical Deep Dive Huawei Cloud CCE supports three container-network models in the V3.5 course: container tunnel network, VPC network and Cloud Native Network 2.0. Option B is correct because the tunnel model uses encapsulation-VXLAN over UDP in the documented implementation-to carry pod traffic across an underlying node network. Open vSwitch acts as a virtual switching component in the tunnel architecture.
Option D is correct because Cloud Native Network 2.0 integrates pod networking with VPC resources. Pod IP addresses can be allocated from the VPC CIDR range, using the applicable enhanced networking and network-interface mechanisms. This reduces encapsulation overhead and supports demanding throughput and latency requirements. Option A is incorrect because a VPN is a service for encrypted connectivity between separate networks, not a named CCE pod-network model. Option C is incorrect because the Kubernetes Service network allocates virtual addresses for Service access; it does not describe the underlying pod- network implementation selected when creating the cluster.
The omitted third legitimate model is VPC network. The question does not offer that option, but its absence does not make the listed choices ambiguous. Architects should select networking based on throughput, latency, IP consumption, integration with security groups, number of nodes and workload scale. Networking performance also depends on underlying ECS specifications and routes. Check container and service CIDRs carefully to avoid overlap with existing VPC or on-premises ranges. Huawei's official model list supports B and D.
NEW QUESTION # 45
High availability is an important design characteristic. Which of the following is not related to high availability?
Answer: C
Explanation:
"HA is a system architecture capability that minimizes single points of failure and provides automatic failover, with the ultimate goal of delivering reliable, uninterrupted services."
[Supporting material] High availability addresses component-level failures; disaster recovery addresses site- level failures and RTO/RPO targets.
Huawei source: Training PDF p. 18
Technical Deep Dive
High availability minimizes interruption from individual component failures. Option A is related because Auto Scaling can maintain necessary application capacity and, when health management is properly integrated, can provision replacement instances. Option B is related because ELB distributes requests across available backends and can exclude unhealthy servers using health checks.
Option D is also related to service continuity. Disaster recovery and backup services protect against more serious events, potentially including a site outage. DR is not synonymous with high availability: HA generally handles component-level failures and automatic failover, while DR focuses on recovery after more extensive disruptions. Nevertheless, they are complementary resilience strategies. Option C , by contrast, specifies a higher network bandwidth upper limit. Bandwidth increases potential throughput; it does not introduce additional service instances, redundancy, healthy failover targets or automated recovery.
For example, upgrading an ECS from a one-Gbit/s to a five-Gbit/s link may reduce congestion during high traffic, but the application still fails if its only server is lost. A more suitable HA architecture distributes instances across availability zones, configures ELB health checks, uses appropriate Auto Scaling policies and implements data-layer redundancy. Architects should evaluate both network performance and resilience, but should not use bandwidth capacity as a substitute for fault tolerance. Thus C alone is unrelated to the HA mechanism specified.
NEW QUESTION # 46
What does Cloud Eye alerting for monitoring metrics allow you to do?
Select all that apply.
Answer: A,B,D
Explanation:
"Users can create alarm rules for key metrics of cloud services. When the conditions in the alarm rule are met, Cloud Eye reports the alarms using emails, SMS, or HTTP/HTTPS requests, enabling users to quickly respond to resource changes."
[The slide lists] flexible policies, varied notifications through HTTP/HTTPS/FunctionGraph/SMS/email, Auto Scaling integration, and default or custom alarm templates.
Huawei source: Training PDF p. 591; official Cloud Eye documentation
Technical Deep Dive
Cloud Eye alarm management allows administrators to define resource- and metric-specific thresholds, triggering rules, notification routes and operational responses. Option A is true because alarm policies can be tailored to individual monitored objects, metrics and events. Option B is true because Huawei supplies standard and customizable templates that can be applied across resources in batches, reducing repetitive configuration and improving policy consistency.
Option D is true. The Huawei V3.5 alarm slide lists HTTP/HTTPS, SMS, email, FunctionGraph and Auto Scaling integration, while related official documentation confirms voice notification channels where supported. A Cloud Eye threshold can notify an operator, invoke an automated function or participate in a scaling policy. For instance, sustained CPU utilization above an agreed threshold can initiate capacity scaling while the operations team receives an alert.
Option C is false because Huawei supports monitoring integration with external observability systems. Cloud Eye metrics can be exposed through suitable exporter and API mechanisms to Prometheus-compatible environments, and those metrics can be visualized in Grafana as part of a broader monitoring architecture.
The claim that such interconnection is categorically unsupported is therefore wrong. Operationally, alarm thresholds must account for duration, evaluation periods, retry behavior, resource scale and alert fatigue. Too- sensitive alerts can generate noise, while overly broad thresholds can miss failures. Automatic scaling requires sensible minimum and maximum capacities, cooldown settings and health checks. A, B and D are the supported capabilities; C is the inaccurate denial of integration.
NEW QUESTION # 47
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