Free and Premium Huawei H13-321_V2.5 Dumps Questions Answers

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."

In OpenCV, an image read into a variable such as img is represented as a NumPy array. The .shape attribute returns the dimensions in the format (height, width, channels). Using img.shape[:2] slices the first two elements, giving the height (h) and width (w). This method is a standard practice for quickly retrieving image dimensions in preprocessing workflows.

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

"OpenCV stores images as NumPy arrays. The shape property returns (height, width, channels). Accessing shape[:2] returns the image height and width."

In themulti-head attentionmechanism:

A:True — the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.

B:True — having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.

C:False — each head has its own learned linear transformations for queries, keys, and values.

D:False — after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.

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

"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result."

A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation-Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.

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

"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models."

Astandard normal distributionis a special case of the normal distribution with:

Mean (μ) = 0

Variance (σ²) = 1This standardization is widely used in statistics and machine learning to normalize features for improved model convergence. Statements A and B are incorrect because variance is never 0 in a valid distribution, and the mean is 0, not 1.

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

"The standard normal distribution is defined with μ = 0 and σ² = 1, providing a normalized scale for statistical analysis."

Maximum likelihood estimation is a statistical method for estimating parameters of a probability distribution by maximizing the likelihood function. To apply MLE, theform of the probability distribution(e.g., normal, exponential) must be known in advance because the likelihood function is defined based on this distribution. Without knowing the distribution type, the estimation process cannot be properly formulated.

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

"MLE assumes that the underlying probability distribution type of the sample data is known and uses it to construct the likelihood function for parameter estimation."

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."

Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an “overly simple” model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model’s complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.

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

"Overfitting means that the trained model performs very well on the training dataset but poorly on new data. It usually results from excessive model complexity, insufficient data, or lack of regularization."

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."

The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):

H (Hue):Defines the type of color (e.g., red, blue).

S (Saturation):Measures vividness — low S means muted colors, high S means vivid colors.

V (Value):Controls brightness — increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.

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

"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis."

TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.

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

"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed-forward networks for sequence modeling."

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."

Word vector evaluation can be:

Intrinsic:Directly tests vector properties via word similarity and analogy tasks.

Extrinsic:Tests in downstream applications.

A:True — word similarity tasks use human-labeled datasets and cosine similarity.

B:True — intrinsic evaluations include similarity and analogy tasks.

C:True — analogy tests assess how well vectors capture semantic relationships.

D:False — both intrinsic and extrinsic methods are valuable, but intrinsic methods are more common for initial evaluations.

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

"Intrinsic evaluations (similarity, analogy) test embedding quality directly, while extrinsic evaluations measure impact on real tasks."

Gamma correction is anon-linearimage processing method used to adjust brightness and contrast. It is not limited to grayscale images — it can be applied to both grayscale and color images by operating on individual channels.

γ < 1:Enhances dark regions (brightens shadows) and compresses highlights.

γ > 1:Enhances bright regions and compresses dark regions.It is based onpower-law (exponential) transformation, making it effective for adjusting human-perceived luminance.

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

"Gamma correction is a non-linear brightness adjustment based on power-law transformation. It applies to both grayscale and color images. For γ<1, dark regions are brightened; for γ>1, bright regions are enhanced."

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."

To address overfitting, HCIP-AI EI Developer V2.5 outlines multiple strategies:

Dropout:A regularization method that randomly ignores certain neurons during training, preventing reliance on specific paths and improving generalization.

Data augmentation:Expands the training dataset by applying transformations (rotation, scaling, flipping) to existing data, increasing diversity and reducing overfitting risk.

Parameter norm penalties:Techniques such as L1 and L2 regularization add a penalty to large parameter values, discouraging overly complex models.

Using amore complex model(Option B) is the opposite of what is recommended, as it generally increases the risk of overfitting.

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

"Common overfitting mitigation techniques include data augmentation to expand datasets, dropout to randomly deactivate neurons during training, and applying regularization penalties to constrain model complexity."

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."

Morphological processing in image analysis is used to process binary or grayscale images based on shape.

Dilation:Expands object boundaries, useful for filling small holes.

Erosion:Shrinks object boundaries, useful for removing noise.By combining them:

Opening:Erosion followed by dilation (removes small objects/noise).

Closing:Dilation followed by erosion (fills small holes).

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

"Morphological processing is based on dilation and erosion. Opening and closing are composite operations derived from these two to handle noise removal and hole filling."