Download Free Huawei H13-321_V2.5 Exam Questions & Answer
Online VALID H13-321_V2.5 Exam Dumps File Instantly
NEW QUESTION # 31
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: C
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 # 32
Which of the following ModelArts training parameters is used to customize hyperparameters?
- A. Algorithm Type
- B. Compute Nodes
- C. Hyperparameter
- D. Resource Pool
Answer: C
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Training Job Parameters
NEW QUESTION # 33
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. Normalize the attention scores to obtain attention weights.
- B. Compute the weighted sum of the value vectors using the attention weights.
- C. Apply a non-linear mapping to the result obtained after the weighted summation.
- D. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
Answer: A,B,D
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 # 34
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics
NEW QUESTION # 35
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
MFCCs are a widely used feature extraction method in speech recognition. The process involves:
* Converting the time-domain signal to the frequency domain using the Fourier transform.
* Mapping the frequency scale to theMel scaleto mimic human hearing perception.
* Taking the logarithm of the power spectrum to emphasize perceptually important differences.
* Applying the discrete cosine transform (DCT) to obtaincepstral coefficients.
These steps capture the spectral envelope, which is important for distinguishing phonemes in speech.
Exact Extract from HCIP-AI EI Developer V2.5:
"MFCCs transform audio to the Mel scale, applying log compression and cepstral transformation to align with human auditory characteristics." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 36
Which of the following are required for the image object detection algorithm?
- A. Confidence calculation
- B. Object location calculation
- C. Object classification determination
- D. Object contour calculation
Answer: A,B,C
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 # 37
Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?
- A. Speech recognition
- B. Image classification
- C. Object detection
- D. Video analytics
Answer: A,B,C,D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Overview
NEW QUESTION # 38
In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
- A. Inversion
- B. Grayscale stretching
- C. Grayscale compression
- D. Gamma correction
Answer: D
Explanation:
When the contrast between bright and dark areas is extreme,gamma correctionis effective in adjusting luminance in a non-linear way to balance these extremes.
* If# < 1, dark areas are brightened, highlights are compressed.
* If# > 1, bright areas are emphasized, shadows are compressed.Other methods like grayscale stretching and compression target linear contrast changes, while inversion flips pixel values but doesn't balance extreme light/dark ranges effectively.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction adjusts image brightness non-linearly, suitable for correcting overly bright or overly dark regions, improving overall visibility." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement
NEW QUESTION # 39
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 # 40
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 # 41
Which of the following is not an acoustic feature of speech?
- A. Frequency
- B. Duration
- C. Amplitude
- D. Semantics
Answer: D
Explanation:
In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such as duration(time length),frequency(pitch), andamplitude(loudness). These features are used in speech recognition and speaker identification systems.
Semantics, on the other hand, refers to the meaning of speech - a linguistic attribute, not an acoustic property. Therefore, it is not classified as an acoustic feature.
Exact Extract from HCIP-AI EI Developer V2.5:
"Speech features include duration, frequency, and amplitude. These are acoustic characteristics, distinct from semantic information, which relates to language meaning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 42
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. 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.
- C. 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.
- D. 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.
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 # 43
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. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
- D. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
Answer: A,B,D
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 # 44
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)
Answer:
Explanation:
Decoder
Explanation:
The Transformer model architecture includes:
* Encoder:Encodes the input sequence into contextualized representations.
* Decoder:Uses the encoder output and self-attention over previously generated tokens to produce the target sequence.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer consists of an encoder-decoder structure, with self-attention mechanisms in both components for sequence-to-sequence learning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Overview
NEW QUESTION # 45
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 # 46
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 # 47
Which of the following statements about the levels of natural language understanding are true?
- A. Syntactic analysis is to find out the meaning of words, structural meaning, their combined meaning, so as to determine the true meaning or concept expressed by a language.
- B. Semantic analysis is to analyze the structure of sentences and phrases to find out the relationship between words and phrases, as well as their functions in sentences.
- C. Speech analysis involves distinguishing independent phonemes from a speech stream based on phoneme rules, and then identifying syllables and their lexemes or words according to the phoneme form rules.
- D. Lexical analysis is to find the lexemes of a word and obtain linguistic information from them.
- E. Pragmatic analysis is to study the influence of the language's external environment on the language users.
Answer: C,D,E
Explanation:
* A:Incorrect - description given matches semantic analysis, not syntactic analysis.
* B:Incorrect - description given matches syntactic analysis, not semantic analysis.
* C:Correct - speech analysis focuses on phoneme recognition and word identification.
* D:Correct - lexical analysis identifies lexemes and retrieves their linguistic details.
* E:Correct - pragmatic analysis studies language use in context and environment.
Exact Extract from HCIP-AI EI Developer V2.5:
"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Levels of Language Understanding
NEW QUESTION # 48
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 # 49
......
H13-321_V2.5 Exam Dumps For Certification Exam Preparation: https://examcollection.actualcollection.com/H13-321_V2.5-exam-questions.html