H13-321_V2.5 Exam Dumps - HCIP - AI EI Developer V2.5 Exam

ModelArts is built to support a variety of compute resources, including CPUs, GPUs, and Ascend AI processors. This heterogeneous resource pool allows users to select the hardware that best matches their training or inference requirements, ensuring cost efficiency and optimal performance for different workloads.

Exact Extract from HCIP-AI EI Developer V2.5:

"ModelArts supports heterogeneous compute environments, enabling selection among CPUs, GPUs, and Ascend processors for flexible AI development."

An object detection system must:

Classifythe detected object (A).

Locatethe object by generating bounding box coordinates (C).

Estimate confidencescores indicating prediction reliability (D).

Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.

Exact Extract from HCIP-AI EI Developer V2.5:

"Object detection includes classification, bounding box localization, and confidence score prediction. Contour detection belongs to segmentation tasks."

ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.

Exact Extract from HCIP-AI EI Developer V2.5:

"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness."

Ethical AI development requires ensuring that large models are trained and deployed in a way that respects laws, fairness, and transparency. This includes preventing bias, ensuring user privacy, protecting intellectual property, and being transparent about data usage and decision-making processes.

Exact Extract from HCIP-AI EI Developer V2.5:

"The development and deployment of large models must follow ethical principles to ensure legal, fair, and transparent use of data, avoiding bias and misuse."

Pixel adjacency describes how pixels are connected:

4-adjacency:Pixels share a side (up, down, left, right).

Diagonal adjacency:Pixels touch at a corner.

8-adjacency:Combination of 4-adjacency and diagonal adjacency.

Given coordinates (21,13) and (22,12), the pixels differ by 1 in both x and y directions, meaning they meet at a corner — this isdiagonal adjacency. Since 8-adjacency includes both side and diagonal adjacency, they are also8-adjacent.

Exact Extract from HCIP-AI EI Developer V2.5:

"In 8-adjacency, pixels are considered neighbors if they are connected horizontally, vertically, or diagonally. Diagonal adjacency occurs when pixels touch at a corner."

ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:

Predictive maintenance in manufacturing to detect potential equipment failures.

Monitoring compliance with dress codes in school or workplace settings.

Detecting unusual sounds in manufacturing or security contexts.

Classifying offerings automatically in e-commerce or retail systems.

Exact Extract from HCIP-AI EI Developer V2.5:

"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification."

A:Incorrect — description given matches semantic analysis, not syntactic analysis.

B:Incorrect — description given matches syntactic analysis, not semantic analysis.

C:Correct — speech analysis focuses on phoneme recognition and word identification.

D:Correct — lexical analysis identifies lexemes and retrieves their linguistic details.

E:Correct — pragmatic analysis studies language use in context and environment.

Exact Extract from HCIP-AI EI Developer V2.5:

"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing."

TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.

Viterbi algorithmis for decoding, not training.

Forward-backward algorithmis part of Baum-Welch’s expectation step but is not a standalone training method.

Exhaustive searchis not a standard HMM training algorithm.

Exact Extract from HCIP-AI EI Developer V2.5:

"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence."