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Huawei - Professional H13-321_V2.5 Exam Dumps

Candidates who pass H13-321_V2.5 Certification prove their worth in the Huawei field. The HCIP-AI-EI Developer V2.5 certification is proof of their competence and skill. This skill is highly useful in big Huawei companies that facilitate a candidate's career. To get certified, it is very important that you pass the HCIP-AI-EI Developer V2.5 certification exam to prove your skills to the tech company. For this task, you require high-quality and accurate prep material to help you out. And many people don't get reliable material and ultimately fail. Failure leads to a loss of time and money.

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q17-Q22):

NEW QUESTION # 17
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 # 18
Which of the following is a learning algorithm used for Markov chains?

A. Exhaustive search
B. Baum-Welch algorithm
C. Forward-backward algorithm
D. Viterbi algorithm

Answer: B

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms

NEW QUESTION # 19
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

A. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
B. Layer normalization accelerates model convergence and does not affect model stability.
C. Residual connections primarily add depth to the model but do not aid in gradient propagation.
D. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.

Answer: A

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 # 20
The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?

A. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
B. Apply a non-linear mapping to the result obtained after the weighted summation.
C. Compute the weighted sum of the value vectors using the attention weights.
D. Normalize the attention scores to obtain attention weights.

Answer: A,C,D

Explanation:
The standardattention mechanisminvolves:
* Computing attention scores via the dot product of query and key vectors (A).
* Applying a normalization function (typically softmax) to obtain attention weights (D).
* Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step
- non-linear mappings are not applied after the weighted sum in the basic attention formula.
Exact Extract from HCIP-AI EI Developer V2.5:
"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Attention Mechanism Fundamentals

NEW QUESTION # 21
If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?

A. Affine transformation
B. Rotational transformation
C. Grayscale transformation
D. Perspective transformation

Answer: A,B

Explanation:
When text in scanned images is tilted,rotational transformationcan correct the angle of the text to align horizontally.Affine transformationcan correct tilt and skew by applying linear transformations such as rotation, scaling, and translation while preserving parallelism of lines. Perspective transformation (A) is used for correcting trapezoidal distortions, while grayscale transformation (B) only adjusts pixel intensity, not orientation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text skew correction can be achieved using rotation and affine transformations, aligning text baselines and improving OCR accuracy." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Transformation

NEW QUESTION # 22
......

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