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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q50-Q55):
NEW QUESTION # 50
Which of the following is not an algorithm for training word vectors?
Answer: B
Explanation:
* Word2VecandFastTextare neural network-based algorithms designed for generating dense vector representations of words.
* BERTis a transformer-based language model that also generates contextualized word embeddings.
* TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.
Exact Extract from HCIP-AI EI Developer V2.5:
"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation
NEW QUESTION # 51
What type of task is viewed when using the Seq2Seq model in speech recognition?
Answer: B
Explanation:
The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set.
Although the output is sequential, each position in the output sequence involves a classification decision.
Exact Extract from HCIP-AI EI Developer V2.5:
"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Models in Speech Recognition
NEW QUESTION # 52
Which of the following are object detection algorithms?
Answer: A,B,C,D
Explanation:
The major families of object detection algorithms include:
* R-CNN (Region-based CNN):Uses region proposals with CNN feature extraction.
* YOLO (You Only Look Once):Performs real-time detection by predicting bounding boxes and class probabilities in a single pass.
* SSD (Single Shot MultiBox Detector):Uses multiple feature maps for detecting objects at different scales in one pass.
* Faster-R-CNN:Improves R-CNN with a Region Proposal Network for speed.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common object detection algorithms include R-CNN, Faster R-CNN, YOLO, and SSD, each using different approaches for balancing accuracy and speed." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection
NEW QUESTION # 53
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
Answer: B
Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation
NEW QUESTION # 54
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Answer:
Explanation:
NER
Explanation:
NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks
NEW QUESTION # 55
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