H13-321_V2.5 Huawei HCIP - AI EI Developer V2.5 Exam Free Practice Exam Questions (2025 Updated)

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."

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."

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."

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."

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."

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 the HSV (Hue, Saturation, Value) color model:

Hrepresents hue (color type).

Srepresents saturation (color intensity or vividness).

Vrepresents brightness.

To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.

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

"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray."

TheVision Transformer (ViT)applies the transformer architecture to image patches. Its key advantage is the use ofself-attentionto capture global dependencies and relationships between all parts of an image. This allows ViT to excel in classification accuracy, especially on large datasets with sufficient pre-training.

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

"ViT applies self-attention to image patches, enabling global feature extraction and improving classification performance compared to local receptive fields in CNNs."

When deploying a pre-trained large model locally for a specific function, themodel development frameworkused during its creation is generally irrelevant unless modifications or retraining are required. However, John must consider:

Output delay— to ensure low latency for real-time Q&A.

Model security— to protect intellectual property and sensitive company data.

Computing power demand— large models require high-performance hardware.

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

"When deploying pre-trained models locally, the deployment plan should address computing resources, performance latency, and security, but does not require re-evaluating the original training framework."

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."

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."

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."

An object detection system must:

Classifythe detected object (A).

Locatethe object by generating bounding box coordinates (C).

Estimate confidencescores indicating prediction reliability (D).

Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.

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

"Object detection includes classification, bounding box localization, and confidence score prediction. Contour detection belongs to segmentation tasks."

The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:

C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.

D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false — large models increase pre-training costs.Bis false — small and domain-specific models still play important roles due to efficiency and deployment constraints.

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

"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models."

In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such asduration(time length),frequency(pitch), andamplitude(loudness). These features are used in speech recognition and speaker identification systems.

Semantics, on the other hand, refers to the meaning of speech — a linguistic attribute, not an acoustic property. Therefore, it is not classified as an acoustic feature.

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

"Speech features include duration, frequency, and amplitude. These are acoustic characteristics, distinct from semantic information, which relates to language meaning."

U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.

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

"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks."

ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:

Predictive maintenance in manufacturing to detect potential equipment failures.

Monitoring compliance with dress codes in school or workplace settings.

Detecting unusual sounds in manufacturing or security contexts.

Classifying offerings automatically in e-commerce or retail systems.

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

"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification."