Free H13-324_V1.0 – HCIP-AI-Ascend Developer V1.0 Practice Test Question

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• ○ Local spatial image features
• ○ Sequential word order patterns
• ○ Tabular database key relations
• ○ Audio sampling rate values

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• ○ Loss function for training
• ○ Activation function in layers
• ○ Optimizer step size control
• ○ Dataset shuffling procedure used

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• ○ Final model file size
• ○ Inference latency per request
• ○ Gradients of loss function
• ○ Dataset label distribution counts

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• ○ Weight initialisation with zeros
• ○ Increasing learning rate sharply
• ○ Removing all hidden layers
• ○ Dropout regularisation during training

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• ○ Measure prediction error magnitude
• ○ Store trained model checkpoints
• ○ Schedule distributed training jobs
• ○ Convert models between frameworks

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• ○ Plain stochastic gradient descent
• ○ Adam adaptive moment estimation
• ○ K-means clustering algorithm used
• ○ Principal component analysis method

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• ○ ReLU rectified linear unit
• ○ Sigmoid logistic activation function
• ○ Adam optimisation algorithm name
• ○ Cross entropy loss function

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• ○ Zero weight initialisation method
• ○ Dropout random unit deactivation
• ○ Data augmentation sample expansion
• ○ Removing the validation set

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• ○ Dataset shuffling each epoch
• ○ Learning rate warmup schedule
• ○ Low precision weight quantization
• ○ Network pruning connection removal

Answer is in the explanation below.

Answer is in the explanation below.

Answer is in the explanation below.