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Pass Guaranteed H13-321_V2.5 - HCIP-AI-EI Developer V2.5 Authoritative Simulated
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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q40-Q45):NEW QUESTION # 40
Which of the following ModelArts training parameters is used to customize hyperparameters?
- A. Compute Nodes
- B. Hyperparameter
- C. Algorithm Type
- D. Resource Pool
Answer: B
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 # 41
Which audio file formats can Huawei Cloud text-to-speech (TTS) generate?
Answer: A,B,C
Explanation:
Huawei Cloud's TTS service supports generating synthesized speech in multiple formats to meet different application requirements:
* WAV:High-quality, uncompressed audio format.
* MP3:Compressed format for efficient storage and streaming.
* PCM:Raw, uncompressed audio data for low-level processing.
AAC is a common format but isnotsupported by Huawei Cloud TTS as per the official specification for HCIP- AI EI Developer V2.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Huawei Cloud TTS supports output in WAV, MP3, and PCM formats to balance audio quality and storage requirements." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Text-to-Speech Service
NEW QUESTION # 42
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 dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
- C. The concatenated output is fed directly into the multi-headed attention mechanism.
- D. Each header's query, key, and value undergo a shared linear transformation to obtain them.
Answer: A,B
NEW QUESTION # 43
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
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
Which of the following methods are useful when tackling overfitting?
- A. Using more complex models
- B. Data augmentation
- C. Using dropout during model training
- D. Using parameter norm penalties
Answer: B,C,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 # 45
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