Pass the Huawei HCIP-AI EI Developer H13-321_V2.5 Questions and answers with ValidTests

The convolutional layer in CNNs is optimized for spatial feature extraction:

Local connectivity(A) reduces computation and memory usage.

Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.

Stride control(C) allows adjusting the output resolution and computational cost.

Sliding kernel operation(D) extracts local patterns without manual feature definition.

Exact Extract from HCIP-AI EI Developer V2.5:

"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers."

TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.

Viterbi algorithmis for decoding, not training.

Forward-backward algorithmis part of Baum-Welch’s expectation step but is not a standalone training method.

Exhaustive searchis not a standard HMM training algorithm.

Exact Extract from HCIP-AI EI Developer V2.5:

"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence."

In Huawei Cloud ModelArts training jobs, theHyperparameterparameter is explicitly designed to allow users to define custom training settings, such as learning rate, batch size, and number of epochs.

Algorithm Typespecifies the model algorithm.

Resource Poolselects the computational environment.

Compute Nodesdetermines the number of nodes used for training.

Exact Extract from HCIP-AI EI Developer V2.5:

"The Hyperparameter field in ModelArts allows users to define and pass custom training parameters to the algorithm for tuning performance."

TheIoUmetric is defined as:

IoU = (Area of Overlap) / (Area of Union)

Numerator:Area of overlap between the predicted bounding box and the ground truth bounding box.

Denominator:Area of union of both bounding boxes.

The statement given in the questionreversesthe numerator and denominator, which is why it is incorrect. IoU is crucial for object detection evaluation, and higher IoU values indicate better localization accuracy.

Exact Extract from HCIP-AI EI Developer V2.5:

"Intersection over Union (IoU) is calculated as the ratio of the intersection area between prediction and ground truth bounding boxes to their union area."

To address overfitting, HCIP-AI EI Developer V2.5 outlines multiple strategies:

Dropout:A regularization method that randomly ignores certain neurons during training, preventing reliance on specific paths and improving generalization.

Data augmentation:Expands the training dataset by applying transformations (rotation, scaling, flipping) to existing data, increasing diversity and reducing overfitting risk.

Parameter norm penalties:Techniques such as L1 and L2 regularization add a penalty to large parameter values, discouraging overly complex models.

Using amore complex model(Option B) is the opposite of what is recommended, as it generally increases the risk of overfitting.

Exact Extract from HCIP-AI EI Developer V2.5:

"Common overfitting mitigation techniques include data augmentation to expand datasets, dropout to randomly deactivate neurons during training, and applying regularization penalties to constrain model complexity."

TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.

Exact Extract from HCIP-AI EI Developer V2.5:

"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed-forward networks for sequence modeling."

In traditional hybridDNN-HMMspeech recognition systems, the DNN is often trained usingframe-level alignmentsgenerated by anHMM-GMMsystem. The GMM serves as an auxiliary tool to perform initial alignments between audio frames and phonetic units, which are then used to train the DNN. Without the HMM-GMM step, supervised training of the DNN in this context is typically not possible.

Exact Extract from HCIP-AI EI Developer V2.5:

"In a DNN-HMM hybrid system, the DNN replaces the GMM in modeling emission probabilities, but GMMs are still used in the initial alignment process to prepare training data for the DNN."

A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation-Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.

Exact Extract from HCIP-AI EI Developer V2.5:

"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models."

In CNN usage:

A:True — color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.

B:True — in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.

C:False — there are clear differences in handling between text and image data.

D:False — CNNs can perform very well in text classification when used appropriately.

Exact Extract from HCIP-AI EI Developer V2.5:

"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count."

The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):

H (Hue):Defines the type of color (e.g., red, blue).

S (Saturation):Measures vividness — low S means muted colors, high S means vivid colors.

V (Value):Controls brightness — increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.

Exact Extract from HCIP-AI EI Developer V2.5:

"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis."