Huawei H13-311_V3.5 Practice Test - Questions Answers, Page 6

The nn module in MindSpore provides essential tools for building neural networks, including:

C . Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.

D . Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.

The other options are incorrect because:

A . Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.

B . Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.

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AI Development Framework: Detailed coverage of MindSpore's nn module, its optimizers, and loss functions.

Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.

AI chips, also known as AI accelerators, are specialized hardware designed to enhance the performance of AI workloads, particularly for tasks like matrix multiplication, which is heavily used in machine learning and deep learning algorithms. These chips optimize operations like matrix multiplications because they are computationally intensive and central to neural network computations (e.g., in forward and backward passes).

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Cutting-edge AI Applications: Discussion of AI chips and accelerators, with a focus on their role in improving computation efficiency.

Deep Learning Overview: Explains how neural network operations like matrix multiplication are optimized in AI hardware.

HarmonyOS provides AI capabilities not only through HMS Core (Huawei Mobile Services Core), but also through other system-level integrations and AI frameworks. While HMS Core is one way to offer AI functionalities, HarmonyOS also has native support for AI processing that can be accessed by external systems or applications beyond HMS Core.

Thus, the statement is false as AI capabilities are not limited solely to HMS Core in HarmonyOS.

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Introduction to Huawei AI Platforms: Covers HarmonyOS and the various ways it integrates AI capabilities into external systems.

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.

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.

Google introduced the concept of the knowledge graph in 2012, and it played a significant role in improving the search engine's ability to understand the relationships between different entities (e.g., people, places, things). This allowed Google to provide richer, more relevant search results by moving from keyword-based search to a more semantic understanding of the user's query. The knowledge graph helps organize information in a more structured way, making it easier for users to find relevant answers quickly and enhancing the overall search experience.

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AI Overview: Discusses the impact of knowledge graphs on search engines and their importance in improving AI-driven user experiences.

Cutting-edge AI Applications: Provides insights into how knowledge graphs are applied in AI systems for improving information retrieval.

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.

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

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.

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