Huawei H13-311_V3.5 Exam Practice Test Instant Access

Question 1

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 2

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 3

In machine learning, which of the following inputs is required for model training and prediction?

ANeural network

BHistorical data

CTraining algorithm

DManual program

Answer : B

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.

Question 4

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 5

An algorithm of unsupervised learning classifies samples in a dataset into several categories. Samples belonging to the same category have high similarity.

ATRUE

BFALSE

Answer : A

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.

Question 6

Which of the following are subfields of AI?

ABackpropagation algorithm

BExpert system

CSmart finance

DComputer vision

Answer : B, D

Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:

Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.

Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.

While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.

Question 7

The concept of "artificial intelligence" was first proposed in the year of:

A1950

B1956

C1960

D1965

Answer : B

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.

Huawei H13-311_V3.5 HCIA-AI V3.5 Exam Practice Test