有効的H13-321_V2.5｜最高のH13-321_V2.5学習指導試験｜試験の準備方法HCIP-AI-EI Developer V2.5対応受験H13-321_V2.5認定はこの分野で大きな効果があり、将来的にもあなたのキャリアに影響を与える可能性があります。 H13-321_V2.5実際の質問ファイルはプロフェッショナルで高い合格率であるため、ユーザーは最初の試行で試験に合格できます。高品質と合格率により、私たちは有名になり、より速く成長しています。多くの受験者は、H13-321_V2.5学習ガイド資料が資格試験に最適なアシスタントであり、学習するために他のトレーニングコースや書籍を購入する必要がなく、試験の前にH13-321_V2.5 HCIP-AI EI Developer試験ブレーンダンプを実践する、彼らは簡単に短時間で試験に合格することができます。 Huawei HCIP-AI-EI Developer V2.5 認定 H13-321_V2.5 試験問題 (Q27-Q32):質問 # 27
Which of the following are the impacts of the development of large models?
A. The accuracy and efficiency of natural language processing tasks will improve
B. Data privacy and security issues will be exacerbated
C. Large models will completely replace small and domain-specific models
D. Model pre-training costs will be reduced
正解：A、B
解説：
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
質問 # 28
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
A. TRUE
B. FALSE
正解：A
解説：
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
質問 # 29
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. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
B. 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.
C. 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.
D. 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".
正解：A、C、D
解説：
Word vector evaluation can be:
* Intrinsicirectly 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
質問 # 30
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 slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
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. 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.
正解：A、B、C、D
解説：
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
質問 # 31
Which of the following ModelArts training parameters is used to customize hyperparameters?
A. Compute Nodes
B. Resource Pool
C. Algorithm Type
D. Hyperparameter
正解：D
解説：
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
irectly tests vector properties via word similarity and analogy tasks.