PracticeVCE H13-321_V2.5 dumps & HCIP-AI EI Developer Sure Practice with 62 Questions
New H13-321_V2.5 Exam Questions| Real H13-321_V2.5 Dumps
NEW QUESTION # 14
Which of the following are required for the image object detection algorithm?
- A. Object classification determination
- B. Object location calculation
- C. Object contour calculation
- D. Confidence calculation
Answer: A,B,D
Explanation:
An object detection system must:
* Classifythe detected object (A).
* Locatethe object by generating bounding box coordinates (C).
* Estimate confidencescores indicating prediction reliability (D).
Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Object detection includes classification, bounding box localization, and confidence score prediction.
Contour detection belongs to segmentation tasks."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow
NEW QUESTION # 15
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)
- A. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
- B. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
- C. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
- D. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
Answer: A,B,C,D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Neural Networks
NEW QUESTION # 16
Which of the following statements about the standard normal distribution are true?
- A. The variance is 1.
- B. The variance is 0.
- C. The mean is 0.
- D. The mean is 1.
Answer: A,C
Explanation:
Astandard normal distributionis a special case of the normal distribution with:
* Mean (#) = 0
* Variance (#²) = 1This standardization is widely used in statistics and machine learning to normalize features for improved model convergence. Statements A and B are incorrect because variance is never 0 in a valid distribution, and the mean is 0, not 1.
Exact Extract from HCIP-AI EI Developer V2.5:
"The standard normal distribution is defined with # = 0 and #² = 1, providing a normalized scale for statistical analysis." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Probability and Statistics Fundamentals
NEW QUESTION # 17
The natural language processing field usually uses distributed semantic representation to represent words.
Each word is no longer a completely orthogonal 0-1 vector, but a point in a multi-dimensional real number space, which is specifically represented as a real number vector.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
Traditional word representations like one-hot vectors are sparse and orthogonal, failing to capture semantic similarities.Distributed semantic representations(word embeddings) map words to dense, continuous vectors in a multi-dimensional space where similar words have similar vector representations. This approach enables better generalization and semantic reasoning in NLP tasks.
Exact Extract from HCIP-AI EI Developer V2.5:
"Distributed semantic representation maps words to dense real-valued vectors in continuous space, allowing semantic similarity to be captured in vector geometry." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation
NEW QUESTION # 18
-------- 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 # 19
If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?
- A. Perspective transformation
- B. Rotational transformation
- C. Grayscale transformation
- D. Affine transformation
Answer: B,D
Explanation:
When text in scanned images is tilted,rotational transformationcan correct the angle of the text to align horizontally.Affine transformationcan correct tilt and skew by applying linear transformations such as rotation, scaling, and translation while preserving parallelism of lines. Perspective transformation (A) is used for correcting trapezoidal distortions, while grayscale transformation (B) only adjusts pixel intensity, not orientation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text skew correction can be achieved using rotation and affine transformations, aligning text baselines and improving OCR accuracy." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Transformation
NEW QUESTION # 20
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. The multi-head attention mechanism captures information about different subspaces within a sequence.
- B. The concatenated output is fed directly into the multi-headed attention mechanism.
- C. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- D. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
Answer: A,D
NEW QUESTION # 21
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
ModelArts is built to support a variety of compute resources, including CPUs, GPUs, and Ascend AI processors. This heterogeneous resource pool allows users to select the hardware that best matches their training or inference requirements, ensuring cost efficiency and optimal performance for different workloads.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts supports heterogeneous compute environments, enabling selection among CPUs, GPUs, and Ascend processors for flexible AI development." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Infrastructure
NEW QUESTION # 22
A text classification task has only one final output, while a sequence labeling task has an output in each input position.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
In NLP:
* Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
* Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Task Categories
NEW QUESTION # 23
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
- A. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
- B. Layer normalization accelerates model convergence and does not affect model stability.
- C. Residual connections primarily add depth to the model but do not aid in gradient propagation.
- 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 # 24
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 # 25
In NLP tasks, transformer models perform well in multiple tasks due to their self-attention mechanism and parallel computing capability. Which of the following statements about transformer models are true?
- A. A transformer model directly captures the dependency between different positions in the input sequence through the self-attention mechanism, without using the recurrent neural network (RNN) or convolutional neural network (CNN).
- B. Multi-head attention is the core component of a transformer model. It computes multiple attention heads in parallel to capture semantic information in different subspaces.
- C. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
- D. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
Answer: A,B,C
Explanation:
Transformers are designed for sequence modeling without recurrence or convolution.
* A:True - self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.
* B:True - multi-head attention computes multiple attention projections in parallel.
* C:True - the architecture is purely attention-based.
* D:False - positional encoding isrequiredbecause self-attention does not inherently encode sequence order.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture
NEW QUESTION # 26
John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?
- A. Model security
- B. Output delay
- C. Demand for computing power
- D. Model development framework
Answer: D
Explanation:
When deploying a pre-trained large model locally for a specific function, themodel development framework used during its creation is generally irrelevant unless modifications or retraining are required. However, John must consider:
* Output delay- to ensure low latency for real-time Q&A.
* Model security- to protect intellectual property and sensitive company data.
* Computing power demand- large models require high-performance hardware.
Exact Extract from HCIP-AI EI Developer V2.5:
"When deploying pre-trained models locally, the deployment plan should address computing resources, performance latency, and security, but does not require re-evaluating the original training framework." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Deployment Considerations
NEW QUESTION # 27
Which of the following methods are useful when tackling overfitting?
- A. Data augmentation
- B. Using dropout during model training
- C. Using more complex models
- D. Using parameter norm penalties
Answer: A,B,D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Preventing Overfitting
NEW QUESTION # 28
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 # 29
Which of the following is not an algorithm for training word vectors?
- A. FastText
- B. TextCNN
- C. Word2Vec
- D. BERT
Answer: B
Explanation:
* 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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation
NEW QUESTION # 30
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
- A. It achieves fast convergence without using pre-trained models.
- B. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
- C. It can process high-resolution images to enhance classification accuracy.
- D. It can handle small datasets with minimal labeling required.
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 # 31
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