H13-311_V3.5 HCIA-AI V3.5 Exam Questions and Answers

Contrary to the statement, Convolutional Neural Networks (CNNs) can indeed be used to process text data. While CNNs are most famously used for image processing, they can also be adapted for natural language processing (NLP) tasks. In text data, CNNs can operate on word embeddings or character-level data to capture local patterns (e.g., sequences of words or characters). CNNs are used in applications such as text classification, sentiment analysis, and language modeling.

The key to CNN's application in text processing is that the convolutional layers can detect patterns in sequences, much like they detect spatial features in images. This versatility is covered in Huawei's HCIA AI platform when discussing CNN's applications beyond image data.

HCIA AI References:

Deep Learning Overview: Explores the usage of CNNs in different domains, including their application in NLP tasks.

Cutting-edge AI Applications: Discusses the use of CNNs in non-traditional tasks, including text and sequential data processing.

The process of constructing a knowledge graph typically involves several key steps:

A. Determining the target domain of the knowledge graph: This defines the scope and boundaries of the information to be represented.

B. Data acquisition: Involves gathering structured and unstructured data from various sources.

D. Knowledge fusion: This step involves integrating and reconciling data from multiple sources to create a consistent and coherent knowledge graph.

Creating new concepts is not typically part of the knowledge graph construction process. Instead, knowledge graphs usually focus on extracting, integrating, and structuring existing knowledge, not creating new concepts.

HCIA AI References:

AI Development Framework: Describes the steps in constructing knowledge graphs, from data acquisition to knowledge fusion and domain determination.

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.

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 References:

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.

OpenCV is a library used primarily for computer vision tasks like image and video processing. It is not considered an AI computing framework in the same way as PyTorch, MindSpore, or TensorFlow, which are commonly used frameworks for developing AI and machine learning models. AI frameworks like PyTorch, TensorFlow, and Huawei’s MindSpore are designed to facilitate the development and deployment of deep learning models.

The global gradient descent algorithm evaluates the gradient over the entire dataset before each update, leading to accurate but slow convergence, especially for large datasets. In contrast, stochastic gradient descent updates the model parameters more frequently, which allows for faster convergence but with noisier updates. While batch gradient descent updates the parameters based on smaller batches of data, none of these algorithms can fully guarantee finding the global minimum in non-convex problems, where local minima may exist.

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.

AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.

In MindSpore, the neural network structure is defined by inheriting the Cell class, which represents a computational node or a layer in the network. Users can customize the network by overriding the __init__ method (for initializing layers) and the construct method (for defining the forward pass of the network). This modular design allows for easy and flexible neural network construction.

Thus, the statement is true because MindSpore's framework allows developers to build neural networks by extending the Cell class and defining custom behavior through the __init__ and construct methods.

HCIA AI References:

AI Development Framework: Detailed coverage of building neural networks in MindSpore, including how to inherit from the Cell class and rewrite key methods for custom network architecture.

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.

The general quantum algorithms include:

A. HHL algorithm (Harrow-Hassidim-Lloyd): An algorithm designed for solving systems of linear equations using quantum computers.

B. Shor algorithm: A quantum algorithm for factoring large integers efficiently, which is important in cryptography.

C. Grover algorithm: A quantum search algorithm used for unstructured database search, providing a quadratic speedup over classical search algorithms.

The A search algorithm* is not a quantum algorithm; it is a classical algorithm used for finding the shortest path in a graph. Therefore, D is incorrect.

HCIA AI References:

Cutting-edge AI Applications: Discusses the potential of quantum algorithms in AI and other advanced computing applications.

In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.

However, the same kernel’s weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.

Thus, the statement that all kernels share weights is false.

HCIA AI References:

Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

AI inference chips are generally simpler than training chips because inference involves running a trained model on new data, which requires fewer computations compared to the training phase. Training chips need to perform more complex tasks like backpropagation, gradient calculations, and frequent parameter updates. Inference, on the other hand, mostly involves forward pass computations, making inference chips optimized for speed and efficiency but not necessarily more complex than training chips.

Thus, the statement is false because inference chips are optimized for simpler tasks compared to training chips.

HCIA AI References:

Cutting-edge AI Applications: Describes the difference between AI inference and training chips, focusing on their respective optimizations.

Deep Learning Overview: Explains the distinction between the processes of training and inference, and how hardware is optimized accordingly.

MindSpore, Huawei's AI framework, includes a data processing engine designed to efficiently handle large datasets during model training. The core feature of this engine is the ability to convert training samples into a format called MindRecord, which optimizes data input and output processes for training. This format ensures that the data pipeline is fast and flexible, providing data efficiently to the training network.

The statement is true because one of MindSpore's core functionalities is to preprocess data and optimize its flow into the neural network training pipeline using the MindRecord format.

HCIA AI References:

Introduction to Huawei AI Platforms: Covers MindSpore's architecture, including its data processing engine and the use of the MindRecord format for efficient data management.

The statement that mini-batch gradient descent (MBGD) takes less time than stochastic gradient descent (SGD) to complete an epoch when GPUs are used for parallel computing is incorrect. Here’s why:

Stochastic Gradient Descent (SGD) updates the weights after each training sample, which can lead to faster updates but more noise in the gradient steps. It completes an epoch after processing all samples one by one.

Mini-batch Gradient Descent (MBGD) processes small batches of data at a time, updating the weights after each batch. While MBGD leverages the computational power of GPUs effectively for parallelization, the comparison made in this question is not about overall computation speed, but about completing an epoch.

MBGD does not necessarily complete an epoch faster than SGD, as MBGD processes multiple samples in each batch, meaning fewer updates per epoch compared to SGD, where weights are updated after every individual sample.

Therefore, the correct answer is B. FALSE, as MBGD does not always take less time than SGD for completing an epoch, even when GPUs are used for parallelization.

HCIA AI References:

AI Development Framework: Discussion of gradient descent algorithms and their efficiency on different hardware architectures like GPUs.