Huawei Cloud Container Instance Service (CCI) can play its advantages in various scenarios. Among the scenarios described below, which scenarios are applicable to CCI? (Multiple choice)

Question

Options

• ABig data, AI computing: applications such as big data, AI training, and reasoning are run in a
• BHigh-performance business: the core large-scale database business requires high equipment
• CScientific computing such as biological gene research and development: high-performance,
• DDevOps continuous delivery: Build a complete DevOps process from code submission to

• A
  83% (24)
• B
  17% (5)

Explanation

Huawei Cloud Container Instance (CCI) is a serverless container service optimized for elastic, on-demand workloads that scale quickly and do not require persistent dedicated infrastructure. Options A, C, and D all fit this profile: big data and AI training jobs are typically batch-style workloads that burst and then release resources; scientific computing like gene research similarly involves intensive but finite compute jobs that benefit from rapid scaling; and DevOps CI/CD pipelines need containers to spin up on-demand for builds, tests, and deployments without long-term resource reservations.

Option B is the distractor because core large-scale database systems require persistent storage, stable high-speed I/O, dedicated hardware, and always-on availability - characteristics that conflict with CCI's serverless, ephemeral model. CCI is not designed to host stateful, latency-sensitive production databases.

Memory tip: Ask yourself whether the workload is "spin up, do the job, tear down." If yes (AI batch jobs, gene sequencing runs, build pipelines), CCI fits. If the workload must be always-on with dedicated hardware and persistent state (like a core database), CCI is the wrong tool.

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