LATEST H13-311_V3.5 TEST TESTKING - STUDY H13-311_V3.5 REFERENCE

Latest H13-311_V3.5 Test Testking - Study H13-311_V3.5 Reference

Latest H13-311_V3.5 Test Testking - Study H13-311_V3.5 Reference

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Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is an internationally recognized certification program that can demonstrate an individual's proficiency in AI concepts and techniques. H13-311_V3.5 exam is designed to test the candidate's knowledge and skills in various areas of AI, such as data mining, deep learning, and neural networks. HCIA-AI V3.5 certification can be a valuable asset for IT professionals who wish to work in AI-related fields or for those who want to expand their knowledge in this area.

Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is a challenging test that requires extensive knowledge and experience in AI. H13-311_V3.5 Exam consists of multiple-choice questions and simulations that assess the candidate's ability to apply AI concepts and techniques in real-world scenarios. To pass the exam, candidates need to have a thorough understanding of AI concepts, algorithms, and programming languages. Overall, the Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is an excellent certification program that can help individuals enhance their AI skills and advance their careers in the IT industry.

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Huawei HCIA-AI V3.5 Sample Questions (Q427-Q432):

NEW QUESTION # 427
Which of the following statements about datasets are true?

  • A. When it comes to the machine learning process, the validation set and the test set are essentially the same.
  • B. In machine learning, a dataset is generally divided into a training set, validation set, and test set.
  • C. A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
  • D. Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.

Answer: B,C,D

Explanation:
In machine learning:
The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample.
A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.
A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).
The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.


NEW QUESTION # 428
Traditional machine learning and deep learning are the core technologies of artificial intelligence.
There is a slight difference in the engineering process. The following steps.
What you don't need to do in deep learning is:

  • A. Model building
  • B. Model evaluation
  • C. Feature engineering
  • D. Data cleaning

Answer: C


NEW QUESTION # 429
L1 with L2 Regularization is a method commonly used in traditional machine learning to reduce generalization errors. The following is about the two.
The right way is:

  • A. L1 with L2 Regularization can be used for feature selection
  • B. L1 Regularization can do feature selection
  • C. L2 Regularization can do feature selection
  • D. L1 with L2 Regularization cannot be used for feature selection

Answer: B


NEW QUESTION # 430
Which of the following options is not a reason for traditional machine learning algorithms to promote the development of deep learning?

  • A. Dimensional disaster
  • B. Feature Engineering
  • C. local invariance and smooth regularization
  • D. Manifold learning

Answer: B


NEW QUESTION # 431
Which of the following functions are provided by the nn module of MindSpore?

  • A. Optimizers such as Momentum and Adam
  • B. Model evaluation indicators such as F1 Score and AUC
  • C. Loss functions such as MSELoss and SoftmaxCrossEntropyWithLogits
  • D. Hyperparameter search modes such as GridSearch and RandomSearch

Answer: A,C

Explanation:
The nn module in MindSpore provides essential tools for building neural networks, including:
C . Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.
D . Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.
The other options are incorrect because:
A . Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.
B . Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.
HCIA AI
Reference:
AI Development Framework: Detailed coverage of MindSpore's nn module, its optimizers, and loss functions.
Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.


NEW QUESTION # 432
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