Huawei HCIP-AI-Model Developer (H13-324) Certification Sample Questions

01. Which Ascend processor deployment scenario is most appropriate for Ascend 310 hardware?
a) Ultra-large distributed foundation-model training
b) Energy-efficient edge inference deployment
c) Enterprise relational database storage
d) Traditional virtualization-only workloads

02. Within the CANN architecture, what is the role of the Runtime layer?
a) Managing system resources and execution.
b) Generating synthetic training data.
c) Compiling graph operator code.
d) Designing neural network layers.

03. What characterizes Parameter-Efficient Fine-Tuning (PEFT)?
a) Full weight updates
b) Larger output files
c) Fewer trainable parameters
d) Longer training time

04. A company fine-tunes a multilingual model using only English training data. Which deployment risk is most likely?
a) Expanded hardware throughput
b) Increased tensor bandwidth
c) Lower checkpoint frequency
d) Reduced multilingual performance

05. When cleaning datasets, why is "Deduplication" required?
a) To prevent over-memorization.
b) To save physical disk space.
c) To speed up network throughput.
d) To encrypt the training data.

06. In a heterogeneous computing system, what is the primary role of the CPU?
a) Tensor acceleration
b) Matrix storage
c) Task orchestration
d) Power management

07. Why are residual connections critical in deep Transformers?
a) Compressing the model size
b) Preventing vanishing gradients
c) Increasing memory usage
d) Normalizing output logits

08. During AI solution planning, what is the primary purpose of business objective analysis?
a) Defining measurable project goals
b) Selecting GPU cooling methods
c) Compressing model parameters
d) Configuring distributed storage

09. You are selecting a base model for an image classification task. If the requirement is to achieve high accuracy on a very large, diverse dataset with significant computational budget, which architectural pattern is generally preferred?
a) A simple linear regression model.
b) An untrained, random weight network.
c) A deep Transformer or Residual Network (ResNet).
d) A shallow, single-layer perceptron.

10. How does "Masked Language Modeling" (MLM) differ from "Causal Language Modeling"?
a) MLM looks at future tokens.
b) Causal look-ahead is restricted to past tokens.
c) MLM cannot process sequences.
d) Causal modeling ignores text.