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The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
A. FALSE
B. TRUE
Answer: A
Explanation:
TheIoUmetric is defined as:
IoU = (Area of Overlap) / (Area of Union)
* Numerator:Area of overlap between the predicted bounding box and the ground truth bounding box.
* Denominator:Area of union of both bounding boxes.
The statement given in the questionreversesthe numerator and denominator, which is why it is incorrect. IoU is crucial for object detection evaluation, and higher IoU values indicate better localization accuracy.
Exact Extract from HCIP-AI EI Developer V2.5:
"Intersection over Union (IoU) is calculated as the ratio of the intersection area between prediction and ground truth bounding boxes to their union area." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics
NEW QUESTION # 34
A text classification task has only one final output, while a sequence labeling task has an output in each input position.
A. TRUE
B. FALSE
Answer: A
Explanation:
In NLP:
* Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
* Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Task Categories
NEW QUESTION # 35
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A. Color image input is multi-channel, whereas text input is single-channel.
B. CNNs are suitable for image tasks, but they perform poorly in text tasks.
C. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
D. For CNN, there is no difference in handling text or image tasks.
Answer: A,C
Explanation:
In CNN usage:
* A:True - color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.
* B:True - in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.
* C:False - there are clear differences in handling between text and image data.
* D:False - CNNs can perform very well in text classification when used appropriately.
Exact Extract from HCIP-AI EI Developer V2.5:
"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP
NEW QUESTION # 36
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)
A. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
B. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
C. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
D. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
Answer: A,B,C,D
Explanation:
The convolutional layer in CNNs is optimized for spatial feature extraction:
* Local connectivity(A) reduces computation and memory usage.
* Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.
* Stride control(C) allows adjusting the output resolution and computational cost.
* Sliding kernel operation(D) extracts local patterns without manual feature definition.
Exact Extract from HCIP-AI EI Developer V2.5:
"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Neural Networks
NEW QUESTION # 37
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
A. Residual connections primarily add depth to the model but do not aid in gradient propagation.
B. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
C. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
D. Layer normalization accelerates model convergence and does not affect model stability.
Answer: C
Explanation:
In Transformers:
* Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing
/exploding gradient issues.
* Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.
Exact Extract from HCIP-AI EI Developer V2.5:
"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Training Mechanisms
NEW QUESTION # 38
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