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

The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set. Although the output is sequential, each position in the output sequence involves a classification decision.

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

"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks."

In feature selection for classification tasks, thechi-square (χ²)statistical test can be applied to evaluate the independence between features and target labels.

In Python’s scikit-learn library, this is implemented using:

python

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from sklearn.feature_selection import SelectKBest, chi2

SelectKBest selects the top K features based on scores returned by the chi2 function.

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

"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic."

In OpenCV:

cv2.imread(filename, 1) reads the image incolor mode.

This loads the image as a3-channelBGR image (Blue, Green, Red).

Other modes: 0 for grayscale, -1 for unchanged (including alpha channel).

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

"When the second parameter of cv2.imread is 1, the image is read in color mode, resulting in a 3-channel BGR image."

ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:

Image classificationfor categorizing visual content.

Object detectionfor locating and identifying multiple objects in images or video frames.

Speech recognitionfor converting speech to text.

Video analyticsfor automated video content analysis.

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

"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics."

Word2VecandFastTextare neural network–based algorithms designed for generating dense vector representations of words.

BERTis a transformer-based language model that also generates contextualized word embeddings.

TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.

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

"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function."

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."

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."

In Transformers:

Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing/exploding gradient issues.

Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.

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

"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence."

ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.

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

"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness."

Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0. This derivative value is always ≤ 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.

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

"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5."