[Dec-2024] Exam Sure Pass Huawei Certification with H13-311_V3.5 exam questions
Real Huawei H13-311_V3.5 Exam Questions Study Guide
Huawei H13-311_V3.5 (HCIA-AI V3.5) exam is a certification exam that focuses on Artificial Intelligence (AI) technologies. It is designed to test an individual's proficiency in AI technologies, including machine learning, deep learning, and neural networks. H13-311_V3.5 exam is also aimed at testing the candidate's ability to design, deploy, and maintain AI solutions in various business environments.
NEW QUESTION # 71
In machine learning, which of the following inputs is required for model training and prediction?
- A. Neural network
- B. Training algorithm
- C. Historical data
- D. Manual program
Answer: C
Explanation:
In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.
Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.
NEW QUESTION # 72
There are a lot of data generated during the training of the neural network. What mechanism does TensorFlow use to avoid excessive input data?
- A. feed
- B. placeholder
- C. Client
- D. fetch
Answer: B
NEW QUESTION # 73
Nesterov is a variant of the momentum optimizer.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
Nesterov Accelerated Gradient (NAG) is indeed a variant of the momentum optimizer. In the traditional momentum method, the gradient is used to adjust the direction based on the current momentum. Nesterov, on the other hand, anticipates the change in the momentum by calculating the gradient at a slightly altered position. This small adjustment leads to better convergence and more efficient optimization, especially in non-convex problems.
Momentum methods and their variants like Nesterov are commonly discussed in the optimization strategies for neural networks, including frameworks such as TensorFlow, which is covered in Huawei's HCIA AI courses.
HCIA AI
Reference:
Deep Learning Overview: Discussion of optimization algorithms, including gradient descent variants like Momentum and Nesterov.
AI Development Framework: Explains the use of Nesterov in deep learning frameworks such as TensorFlow and PyTorch.
NEW QUESTION # 74
The following code was used when compiling the model:
model.compile(optimizer='Adam,loss='categorical.crossentropy',metrics=[tf.keras.metrics.accurac y]), currently using evaluate When the method evaluates the model, which of the following indicators will be output?
- A. categorical accuracy
- B. accuracy
- C. categorical_ 1oss
- D. loss
Answer: B,D
NEW QUESTION # 75
According to the definition of information entropy what is the bit entropy of throwing a uniform coin?
- A. 0.5
- B. 0
- C. 1
- D. 2
Answer: B
NEW QUESTION # 76
Which of the following functions can numerically stabilize overflow and underflow?
- A. Softplus function
- B. Softminus function
- C. Softmin func!Jon
- D. Soft max function
Answer: D
NEW QUESTION # 77
Global gradient descent algorithm, stochastic gradient descent algorithm and batch gradient descent algorithm are all gradient descent algorithms. The following is wrong about its advantages and disadvantages.
- A. Batch gradient algorithm can solve the local minimum problem
- B. Stochastic gradient algorithm can find the minimum value of the loss function
- C. The global gradient algorithm can find the minimum value of the loss function
- D. The convergence process of the global gradient algorithm is time-consuming
Answer: B
NEW QUESTION # 78
What are the results returned by the if conditional statements in the Python language? (Multiple choice)
- A. 0
- B. True
- C. FALSE
- D. null
Answer: A,C
NEW QUESTION # 79
The concept of "artificial intelligence" was first proposed in the year of:
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
The concept of "artificial intelligence" was first formally introduced in 1956 during the Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. This event is widely regarded as the birth of AI as a field of study. The conference aimed to explore the idea that human intelligence could be simulated by machines, laying the groundwork for subsequent AI research and development.
This date is significant in the history of AI because it marked the beginning of a concentrated effort to develop machines that could mimic cognitive functions such as learning, reasoning, and problem-solving.
NEW QUESTION # 80
HUAWEI HiAI Engine Can easily combine multiple AI Ability and App integrated.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 81
The Python list can be identified by `[]", and the default index of the first element from left to right is 1.
- A. False
- B. True
Answer: A
NEW QUESTION # 82
The following applications that are not part of the adversarial generation network are?
- A. Data enhancement
- B. Text generation
- C. Image generation
- D. Image Identification
Answer: D
NEW QUESTION # 83
Which of the following statements are true about decision trees?
- A. Quantitative indicators of purity can only be obtained by using information entropy.
- B. A key step to building a decision tree involves dividing all feature attributes and comparing the purity of the division's result sets.
- C. The common decision tree algorithms include ID3, C4.5, and CART.
- D. Building a decision tree means selecting feature attributes and determining their tree structure.
Answer: B,C,D
Explanation:
A . TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.
B . FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.
C . TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.
D . TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.
HCIA AI
Reference:
Machine Learning Overview: Covers decision tree algorithms and their use cases.
Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.
NEW QUESTION # 84
Which of the following are AI capabilities provided by the HMS Core?
- A. MindSpore Lite
- B. ML Kit
- C. HiAI Foundation
- D. HiAI Engine
Answer: B,C,D
Explanation:
Huawei HMS Core (Huawei Mobile Services Core) provides a variety of AI capabilities, including:
HiAI Foundation: Offers basic AI infrastructure, enabling AI computing capabilities.
HiAI Engine: Provides pre-built AI engines for tasks like image processing and NLP.
ML Kit: Provides machine learning features for developers to integrate into apps.
MindSpore Lite is not part of HMS Core but rather a lightweight version of the MindSpore framework designed for mobile and edge devices.
NEW QUESTION # 85
Which of the following is NOT a key feature that enables all-scenario deployment and collaboration for MindSpore?
- A. Graph optimization based on a software-hardware synergy shields the differences between scenarios.
- B. Data and computing graphs are transmitted to Ascend AI Processors.
- C. Unified model IR delivers a consistent deployment experience.
- D. Federal meta-learning enables real-time, coordinated model updates between different devices, and across the device and cloud.
Answer: D
Explanation:
While MindSpore supports all-scenario deployment with features like data and computing graph transmission to Ascend AI processors, unified model IR for consistent deployment, and graph optimization based on software-hardware synergy, federal meta-learning is not explicitly a core feature of MindSpore's deployment strategy. Federal meta-learning refers to a distributed learning paradigm, but MindSpore focuses more on efficient computing and model optimization across different environments.
NEW QUESTION # 86
Deep learning is different from machine learning and there are no unsupervised algorithms
- A. False
- B. True
Answer: A
NEW QUESTION # 87
In May 1997, the famous "Human-Machine Wars" final computer defeated Kasparov, the world chess king, with a total score of 3.5 to 2 5. is this computer called?
- A. Dark blue
- B. Blue sky
- C. Dark green
- D. Ponder
Answer: A
NEW QUESTION # 88
Tensorflow Operations and Computation Graph are not - run in the Session
- A. False
- B. True
Answer: A
NEW QUESTION # 89
As shown in the figure below, what is the value of the determinant A?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: A
NEW QUESTION # 90
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