[Oct 01, 2025] Prepare For The H13-321_V2.5 Question Papers In Advance
H13-321_V2.5 PDF Dumps Real 2025 Recently Updated Questions
NEW QUESTION # 29
Which of the following are the impacts of the development of large models?
- A. Large models will completely replace small and domain-specific models
- B. The accuracy and efficiency of natural language processing tasks will improve
- C. Data privacy and security issues will be exacerbated
- D. Model pre-training costs will be reduced
Answer: B,C
Explanation:
The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:
* C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.
* D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false - large models increase pre-training costs.Bis false - small and domain-specific models still play important roles due to efficiency and deployment constraints.
Exact Extract from HCIP-AI EI Developer V2.5:
"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Trends and Challenges
NEW QUESTION # 30
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
NEW QUESTION # 31
Which of the following methods are useful when tackling overfitting?
- A. Using dropout during model training
- B. Data augmentation
- C. Using parameter norm penalties
- D. Using more complex models
Answer: A,B,C
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 # 32
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. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
- B. 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).
- C. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
- D. 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.
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 # 33
Which of the following statements about the functions of the encoder and decoder is true?
- A. The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
- B. The output lengths of the encoder and decoder are the same.
- C. The encoder converts context vectors into variable-length output sequences.
- D. The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
Answer: A
Explanation:
In anencoder-decoderarchitecture:
* Theencoderprocesses variable-length inputs and encodes them into fixed-length context vectors that summarize the input. (Cis correct.)
* Thedecodergenerates output sequences from this context, which may be of variable length.
* Adescribes the decoder incorrectly;Bmixes roles;Dis false because output length depends on the target sequence, not the encoder output length.
Exact Extract from HCIP-AI EI Developer V2.5:
"The encoder transforms variable-length sequences into context vectors, which the decoder uses to generate variable-length outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Functions
NEW QUESTION # 34
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 # 35
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 # 36
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
- A. Sigmoid
- B. SeLU
- C. Tanh
- D. ReLU
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks
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. Video analytics
- B. Object detection
- C. Image classification
- D. Speech recognition
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
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 # 39
Which of the following ModelArts training parameters is used to customize hyperparameters?
- A. Resource Pool
- B. Compute Nodes
- C. Hyperparameter
- D. Algorithm Type
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 # 40
Which of the following applications are supported by ModelArts ExeML?
- A. Dress code conformance monitoring in campuses
- B. Anomalous sound detection in production or security scenarios
- C. Automatic offering classification
- D. Predictive maintenance of manufacturing equipment
Answer: A,B,C,D
Explanation:
ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:
* Predictive maintenance in manufacturing to detect potential equipment failures.
* Monitoring compliance with dress codes in school or workplace settings.
* Detecting unusual sounds in manufacturing or security contexts.
* Classifying offerings automatically in e-commerce or retail systems.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts ExeML Application Scenarios
NEW QUESTION # 41
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 # 42
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. Output delay
- B. Demand for computing power
- C. Model security
- 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 # 43
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
- A. TRUE
- B. FALSE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models
NEW QUESTION # 44
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