Huawei H13-323_V1.0 Practice Exams
Last updated on Oct 21,2026- Exam Code: H13-323_V1.0
- Exam Name: HCIP-AI Solution Architect V1.0
- Certification Provider: Huawei
- Latest update: Oct 21,2026
Which of the following descriptions of large model-related technologies is correct? (Multiple choice)
- A . Contextual learning can help the algorithm better understand the meaning and relationship of words in a sentence, or help the algorithm better identify the relationship between different objects in an image.
- B . Thinking chain requires the model to explicitly output the intermediate step-by-step reasoning steps before outputting the final answer, so that large models can perform some relatively complex reasoning tasks
- C . Instruction fine-tuning is to construct an instance of the instruction format and then fine-tune the large language model in a supervised manner
- D . In-Context Learning combines intermediate reasoning steps to introduce prompts into the final output
What is the core idea of systematic AI?
- A . Focus on single-task optimization
- B . Multi-level construction and atomic capabilities
- C . Applicable only to visual identification
- D . Only applicable to text processing
Which of the following features does Python have? (Multiple choice)
- A . Explanatory
- B . Open Source
- C . Simple
- D . Object-oriented
MindFormers distributed training and reasoning require the conversion of complete weights to distributed weights, which can be automatically converted through the () parameter in the configuration file.
HuggingFace Transformers large model suite and DeepSpeed acceleration library are both built based on the PyTorch framework and cannot run on Ascend NPU.
- A . True
- B . False
Which variable can only be used in the current function?
- A . Both are OK
- B . Global variables
- C . Local variables
- D . Module variables
Which of the following features does Python have? (Multiple choice)
- A . Explanatory
- B . Open Source
- C . Simple
- D . Object-oriented
What data types does TensorFlow use?
- A . Scalar
- B . Matrix
- C . Vector
- D . Tensor
No data cleaning is required when training large models.
- A . True
- B . False