Huawei H13-321_V2.5 HCIP - AI EI Developer V2.5 Exam Exam Practice Test

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."

Transformers are designed for sequence modeling without recurrence or convolution.

A:True — self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.

B:True — multi-head attention computes multiple attention projections in parallel.

C:True — the architecture is purely attention-based.

D:False — positional encoding isrequiredbecause self-attention does not inherently encode sequence order.

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

"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order."

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."

In hybridDNN-HMMspeech recognition:

TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.

TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.

This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.

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

"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech."

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."

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."

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."

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."

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."

In Python'sjiebalibrary, the cut() method is used for Chinese word segmentation. It splits a given sentence into individual words based on probabilistic models and a dictionary. The method supports both precise mode and full mode, with precise mode being the default for balanced accuracy and completeness.

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

"The jieba.cut() method segments Chinese text into words, supporting multiple modes for different application needs."

In traditional hybridDNN-HMMspeech recognition systems, the DNN is often trained usingframe-level alignmentsgenerated by anHMM-GMMsystem. The GMM serves as an auxiliary tool to perform initial alignments between audio frames and phonetic units, which are then used to train the DNN. Without the HMM-GMM step, supervised training of the DNN in this context is typically not possible.

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

"In a DNN-HMM hybrid system, the DNN replaces the GMM in modeling emission probabilities, but GMMs are still used in the initial alignment process to prepare training data for the DNN."

The Transformer model architecture includes:

Encoder:Encodes the input sequence into contextualized representations.

Decoder:Uses the encoder output and self-attention over previously generated tokens to produce the target sequence.

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

"The Transformer consists of an encoder-decoder structure, with self-attention mechanisms in both components for sequence-to-sequence learning."

Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.

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

"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models."

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."

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."

ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:

Image classificationfor categorizing visual content.

Object detectionfor locating and identifying multiple objects in images or video frames.

Speech recognitionfor converting speech to text.

Video analyticsfor automated video content analysis.

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

"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics."

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."

Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.

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

"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification."