Which of the following descriptions about the applicable scenarios of the Huawei container high- performance batch computing solution are correct? (Multiple choice)

Question

Options

• AAI scenarios: multi-computing power is available out of the box, natively supporting a variety of
• BDatabase scenarios, exclusive resource sharing, network isolation, and performance assurance
• CBig data scenarios: separation of storage and computing, second-level expansion and contraction
• DGene sequencing scenario, supports 100,000+ container-scale concurrency, and easily handles

• A
  71% (12)
• B
  29% (5)

Explanation

Huawei's container high-performance batch computing solution is designed for workloads that are compute-intensive, massively parallel, and episodic in nature. AI training and inference (A) fits perfectly because these jobs run in discrete bursts and benefit from out-of-the-box support for heterogeneous accelerators like GPUs and NPUs. Big data processing (C) is a natural fit because frameworks like Spark rely on separating storage from compute and need rapid elastic scaling to handle variable workloads. Gene sequencing (D) involves enormous parallel job queues that demand the kind of 100,000+ concurrent container scale that batch computing delivers efficiently.

Option B is the distractor. Database workloads are persistent, stateful, and require stable, long-running connections with guaranteed I/O, which is the opposite of what batch computing provides. Databases belong to a dedicated database hosting solution, not a batch scheduler.

Memory tip: Batch computing serves workloads that start, run hard, and stop. If you can describe the work as "a job," it fits batch computing. Databases are never "a job" - they are always running, so they do not belong in this category.

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