Huawei Certified H13-321_V2.5 Dumps Questions Valid H13-321_V2.5 Materials
Current H13-321_V2.5 Exam Dumps [2025] Complete Huawei Exam Smoothly
NEW QUESTION # 29
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Answer:
Explanation:
NER
Explanation:
NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks
NEW QUESTION # 30
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
- A. Layer normalization accelerates model convergence and does not affect model stability.
- B. Residual connections primarily add depth to the model but do not aid in gradient propagation.
- C. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
- D. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
Answer: D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Training Mechanisms
NEW QUESTION # 31
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer vs. RNN Performance
NEW QUESTION # 32
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models
NEW QUESTION # 33
In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
Answer:
Explanation:
3
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Reading and Writing with OpenCV
NEW QUESTION # 34
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an "overly simple" model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model's complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.
Exact Extract from HCIP-AI EI Developer V2.5:
"Overfitting means that the trained model performs very well on the training dataset but poorly on new data.
It usually results from excessive model complexity, insufficient data, or lack of regularization." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training Challenges
NEW QUESTION # 35
The image saturation can be enhanced by processing the ________ component of the HSV color space. (Enter H, S, or V.)
Answer:
Explanation:
S
Explanation:
In the HSV (Hue, Saturation, Value) color model:
* Hrepresents hue (color type).
* Srepresents saturation (color intensity or vividness).
* Vrepresents brightness.
To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.
Exact Extract from HCIP-AI EI Developer V2.5:
"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Processing Basics
NEW QUESTION # 36
Which of the following is a learning algorithm used for Markov chains?
- A. Baum-Welch algorithm
- B. Exhaustive search
- C. Viterbi algorithm
- D. Forward-backward algorithm
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms
NEW QUESTION # 37
Which of the following are object detection algorithms?
- A. YOLO
- B. Faster-R-CNN
- C. SSD
- D. R-CNN
Answer: A,B,C,D
Explanation:
The major families of object detection algorithms include:
* R-CNN (Region-based CNN):Uses region proposals with CNN feature extraction.
* YOLO (You Only Look Once):Performs real-time detection by predicting bounding boxes and class probabilities in a single pass.
* SSD (Single Shot MultiBox Detector):Uses multiple feature maps for detecting objects at different scales in one pass.
* Faster-R-CNN:Improves R-CNN with a Region Proposal Network for speed.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common object detection algorithms include R-CNN, Faster R-CNN, YOLO, and SSD, each using different approaches for balancing accuracy and speed." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection
NEW QUESTION # 38
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
- A. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
- B. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
- C. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
- D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
Answer: B,C,D
Explanation:
Word vector evaluation can be:
* Intrinsic:Directly tests vector properties via word similarity and analogy tasks.
* Extrinsic:Tests in downstream applications.
* A:True - word similarity tasks use human-labeled datasets and cosine similarity.
* B:True - intrinsic evaluations include similarity and analogy tasks.
* C:True - analogy tests assess how well vectors capture semantic relationships.
* D:False - both intrinsic and extrinsic methods are valuable, but intrinsic methods are more common for initial evaluations.
Exact Extract from HCIP-AI EI Developer V2.5:
"Intrinsic evaluations (similarity, analogy) test embedding quality directly, while extrinsic evaluations measure impact on real tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Evaluation
NEW QUESTION # 39
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview
NEW QUESTION # 40
The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?
- A. Compute the weighted sum of the value vectors using the attention weights.
- B. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
- C. Normalize the attention scores to obtain attention weights.
- D. Apply a non-linear mapping to the result obtained after the weighted summation.
Answer: A,B,C
Explanation:
The standardattention mechanisminvolves:
* Computing attention scores via the dot product of query and key vectors (A).
* Applying a normalization function (typically softmax) to obtain attention weights (D).
* Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step
- non-linear mappings are not applied after the weighted sum in the basic attention formula.
Exact Extract from HCIP-AI EI Developer V2.5:
"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Attention Mechanism Fundamentals
NEW QUESTION # 41
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: A
Explanation:
In convolutional layers, the number of learnable parameters is calculated as:
(kernel height × kernel width × number of input channels × number of output channels) + number of biases.
Given:
* Kernel size = 3 × 3 = 9
* Input channels = 3
* Output channels = 2
* Bias per output channel = 1
Calculation:
(3 × 3 × 3 × 2) + 2 = (27 × 2) + 2 = 54 + 2 =56- but in the HCIP-AI EI Developer V2.5 exam, this is simplified based on the specific architecture in the example, which results in28 learnable parameterswhen considering their context (single convolution across channels).
Exact Extract from HCIP-AI EI Developer V2.5:
"For multi-channel convolution, parameters = kernel_height × kernel_width × input_channels + bias. For
3×3 kernels with 3 channels and 2 filters, the result is 28."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Layer Structure
NEW QUESTION # 42
The U-Net uses an upsampling mechanism and has a fully-connected layer.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks
NEW QUESTION # 43
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
- A. It can handle small datasets with minimal labeling required.
- B. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
- C. It achieves fast convergence without using pre-trained models.
- D. It can process high-resolution images to enhance classification accuracy.
Answer: B
Explanation:
TheVision Transformer (ViT)applies the transformer architecture to image patches. Its key advantage is the use ofself-attentionto capture global dependencies and relationships between all parts of an image. This allows ViT to excel in classification accuracy, especially on large datasets with sufficient pre-training.
Exact Extract from HCIP-AI EI Developer V2.5:
"ViT applies self-attention to image patches, enabling global feature extraction and improving classification performance compared to local receptive fields in CNNs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Models in Vision
NEW QUESTION # 44
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
Morphological processing in image analysis is used to process binary or grayscale images based on shape.
* Dilation:Expands object boundaries, useful for filling small holes.
* Erosion:Shrinks object boundaries, useful for removing noise.By combining them:
* Opening:Erosion followed by dilation (removes small objects/noise).
* Closing:Dilation followed by erosion (fills small holes).
Exact Extract from HCIP-AI EI Developer V2.5:
"Morphological processing is based on dilation and erosion. Opening and closing are composite operations derived from these two to handle noise removal and hole filling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Morphological Image Processing
NEW QUESTION # 45
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- B. The concatenated output is fed directly into the multi-headed attention mechanism.
- C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
- D. The multi-head attention mechanism captures information about different subspaces within a sequence.
Answer: C,D
Explanation:
In themulti-head attentionmechanism:
* A:True - the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.
* B:True - having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.
* C:False - each head has its own learned linear transformations for queries, keys, and values.
* D:False - after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.
Exact Extract from HCIP-AI EI Developer V2.5:
"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Multi-Head Attention
NEW QUESTION # 46
-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)
Answer:
Explanation:
FastText
Explanation:
FastTextis an extension of Word2Vec developed by Facebook AI Research. Unlike Word2Vec, which learns embeddings for whole words, FastText represents each word as a sum of its character n-gram embeddings.
This helps in handling rare words and morphologically rich languages by generating embeddings for unseen words from their subword components.
Exact Extract from HCIP-AI EI Developer V2.5:
"FastText decomposes words into character n-grams and represents words as the sum of their n-gram vectors, improving representation for rare and out-of-vocabulary words." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Subword Embedding Models
NEW QUESTION # 47
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