Huawei H13-311_V3.5 Exam Questions Instant Access

Question 1

Which of the following statements about datasets are true?

ATesting 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.

BA 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.

CIn machine learning, a dataset is generally divided into a training set, validation set, and test set.

DWhen it comes to the machine learning process, the validation set and the test set are essentially the same.

Answer : A, B, C

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.

Question 2

Which of the following are feedforward neural networks?

AFully-connected neural networks

BRecurrent neural networks

CBoltzmann machines

DConvolutional neural networks

Answer : A, D

Feedforward neural networks (FNNs) are networks where information moves in only one direction---forward---from the input nodes through hidden layers to the output nodes. Both fully-connected neural networks (where each neuron in one layer connects to every neuron in the next) and convolutional neural networks (CNNs) (which have a specific architecture for image data) are examples of feedforward networks.

However, recurrent neural networks (RNNs) and Boltzmann machines are not feedforward networks. RNNs include loops where information can be fed back into previous layers, and Boltzmann machines involve undirected connections between units, making them a form of a stochastic network rather than a feedforward structure.

Question 3

Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?

Ak-NN typically uses the mean value method to predict regression.

Bk-NN typically uses the majority voting method to predict classification.

Ck-NN is a parametric method often used for datasets with regular decision boundaries.

DThe k-NN algorithm determines which class an object belongs to based on the class to which most of the object's k nearest neighbors belong.

Answer : B, D

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.

Question 4

Huawei Cloud EI provides knowledge graph, OCR, machine translation, and the Celia (virtual assistant) development platform.

ATRUE

BFALSE

Answer : A

Huawei Cloud EI (Enterprise Intelligence) provides a variety of AI services and platforms, including knowledge graph, OCR (Optical Character Recognition), machine translation, and the Celia virtual assistant development platform. These services enable businesses to integrate AI capabilities such as language processing, image recognition, and virtual assistant development into their systems.

Question 5

Huawei Cloud ModelArts provides ModelBox for device-edge-cloud joint development. Which of the following are its optimization policies?

AHardware affinity

BOperator optimization

CAutomatic segmentation of operators

DModel replication

Answer : A, B, C

Huawei Cloud ModelArts provides ModelBox, a tool for device-edge-cloud joint development, enabling efficient deployment across multiple environments. Some of its key optimization policies include:

Hardware affinity: Ensures that the models are optimized to run efficiently on the target hardware.

Operator optimization: Improves the performance of AI operators for better model execution.

Automatic segmentation of operators: Automatically segments operators for optimized distribution across devices, edges, and clouds.

Model replication is not an optimization policy offered by ModelBox.

Question 6

Which of the following statements is false about the debugging and application of a regression model?

AIf the model does not meet expectations, you need to use data cleansing and feature engineering.

BAfter model training is complete, you need to use the test dataset to evaluate your model so that its generalization capability meets expectations.

CIf overfitting occurs, you can add a regularization term to the Lasso or ridge regression and adjust hyperparameters.

DIf underfitting occurs, you can use a more complex regression model, for example, logistic regression.

Answer : D

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.

Question 7

What are the application scenarios of computer vision?

AVideo action analysis

BImage search

CSmart albums

DVoice navigation

Answer : A, B, C

Computer vision, a subfield of AI, has various application scenarios that involve the analysis and understanding of images and videos. Some key application scenarios include:

Video action analysis: Identifying and analyzing human actions or movements in videos.

Image search: Using visual information to search for similar images in large databases.

Smart albums: Organizing and categorizing photos using AI-based image recognition algorithms to group them by themes, people, or events.

Voice navigation is a part of natural language processing and speech recognition, not computer vision.

Huawei HCIA-AI V3.5 H13-311_V3.5 Exam Questions