Which of the following algorithms presents the most chaotic landscape on the loss surface?
A
Explanation:
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it
updates the model parameters for each individual training example, which can introduce a significant
amount of noise into the optimization process. This leads to a less smooth and more chaotic path
toward the global minimum compared to methods like batch gradient descent or mini-batch gradient
descent, which provide more stable updates.
Reference: Huawei HCIA-AI Certification, Machine Learning Algorithms.
Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?
B, D
Explanation:
The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification
and regression. In classification tasks, it typically uses majority voting to assign a label to a new
instance based on the most common class among its nearest neighbors. The algorithm works by
calculating the distance (often using Euclidean distance) between the query point and the points in
the dataset, and then assigning the query point to the class that is most frequent among its k nearest
neighbors.
For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest
neighbors, although this is less common than its classification use.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.
An algorithm of unsupervised learning classifies samples in a dataset into several categories.
Samples belonging to the same category have high similarity.
A
Explanation:
In unsupervised learning, the goal is to find hidden patterns or intrinsic structures in input data
without labeled outcomes. One common unsupervised learning task is clustering, where an
algorithm groups the dataset into several categories or clusters. Samples within the same cluster
have high similarity based on certain features, while samples in different clusters have low similarity.
Examples of clustering algorithms include k-means and hierarchical clustering.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.
Which of the following statements is false about the debugging and application of a regression
model?
D
Explanation:
Logistic regression is not a solution for underfitting in regression models, as it is used primarily for
classification problems rather than regression tasks. If underfitting occurs, it means that the model is
too simple to capture the underlying patterns in the data. Solutions include using a more complex
regression model like polynomial regression or increasing the number of features in the dataset.
Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data
cleansing and feature engineering are correct methods for improving model performance.
Reference: Huawei HCIA-AI Certification, AI Model Debugging and Optimization.
In machine learning, which of the following inputs is required for model training and prediction?
B
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.
Reference: Huawei HCIA-AI Certification, Machine Learning Workflow.
Which of the following statements about datasets are true?
A, B, C
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.
Reference: Huawei HCIA-AI Certification, Machine Learning Data and Evaluation.
In a hyperparameter-based search, the hyperparameters of a model are searched based on the data
on and the model's performance metrics.
A
Explanation:
In machine learning, hyperparameters are the parameters that govern the learning process and are
not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part
of improving a model's performance. The goal of a hyperparameter-based search is to find the set of
hyperparameters that maximizes the model's performance on a given dataset.
There are different techniques for hyperparameter tuning, such as grid search, random search, and
more advanced methods like Bayesian optimization. The performance of the model is assessed
based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are
adjusted accordingly to achieve the best performance.
In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both
traditional machine learning models and deep learning frameworks. The course emphasizes the
importance of selecting appropriate hyperparameters and demonstrates how frameworks such as
TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize
models efficiently.
HCIA AI
Reference:
AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in
model training.
Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network
architectures, including tuning learning rates, batch sizes, and other key parameters.
AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like
TensorFlow and Huawei ModelArts.
The general process of building a project using machine learning involves the following steps: split
data, _________________ the model, deploy the model the model, and fine-tune the model.
train
When feature engineering is complete, which of the following is not a step in the decision tree
building process?
D
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.
Which of the following statements are true about decision trees?
A, 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.