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Types of iPassleaderHuawei H13-311_V3.5 Exam Questions

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Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is a certification exam that is designed to test candidates' knowledge and skills in the field of Artificial Intelligence (AI). H13-311_V3.5 exam is a part of the Huawei certification program and is aimed at professionals who want to demonstrate their expertise in AI technologies. H13-311_V3.5 exam covers a range of topics related to AI, including machine learning, natural language processing, deep learning, and computer vision.

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Huawei HCIA-AI V3.5 Sample Questions (Q219-Q224):

NEW QUESTION # 219
In random forest, what strategy is used to determine the outcome of the final ensemble model?

A. Find the average
B. Voting system
C. Cumulative system
D. Cumulative system

Answer: A,B

NEW QUESTION # 220
Python script execution mode includes interactive mode and script mode

A. True
B. False

Answer: A

NEW QUESTION # 221
During application development, which of the following operations is not a typical network definition?

A. Weight initialization
B. Network definition
C. Knowledge distillation
D. Network execution

Answer: C

NEW QUESTION # 222
Which is not a Python operator?

A. Comparison operator
B. arithmetic operator
C. Logical operator
D. Reasoning operator

Answer: D

NEW QUESTION # 223
When feature engineering is complete, which of the following is not a step in the decision tree building process?

A. Pruning
B. Feature selection
C. Data cleansing
D. Decision tree generation

Answer: C

Explanation:
When building a decision tree, the steps generally involve:
Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.
Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.
Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.
Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.
HCIA AI
Reference:
Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.
AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.

NEW QUESTION # 224
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