[Mar 08, 2026] H13-321_V2.5 Dumps PDF and Test Engine Exam Questions - Actual4test
Verified H13-321_V2.5 exam dumps Q&As with Correct 62 Questions and Answers
NEW QUESTION # 10
Which of the following has never been used as a method in the history of NLP?
- A. Statistics-based method
- B. Rule-based method
- C. Deep learning-based method
- D. Recursion-based method
Answer: D
Explanation:
Historically, NLP has evolved through three main methodological phases:
* Rule-based methods- used in early systems, relying on manually crafted grammar and lexicons.
* Statistics-based methods- introduced probabilistic models such as HMMs and n-grams.
* Deep learning-based methods- using neural networks, transformers, and embeddings.
A "recursion-based method" has never been recognized as a distinct NLP methodology, even though recursion can appear in linguistic theory, it is not a primary computational approach in NLP history.
Exact Extract from HCIP-AI EI Developer V2.5:
"The evolution of NLP includes rule-based, statistical, and deep learning-based methods. Recursion-based approaches are not considered a formal method in NLP development history." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Development History
NEW QUESTION # 11
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
- A. Tanh
- B. ReLU
- C. SeLU
- D. Sigmoid
Answer: D
Explanation:
Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0.
This derivative value is always # 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks
NEW QUESTION # 12
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 # 13
When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:
Answer:
Explanation:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods
NEW QUESTION # 14
Which of the following applications are supported by ModelArts ExeML?
- A. Automatic offering classification
- B. Anomalous sound detection in production or security scenarios
- C. Dress code conformance monitoring in campuses
- D. Predictive maintenance of manufacturing equipment
Answer: A,B,C,D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts ExeML Application Scenarios
NEW QUESTION # 15
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Morphological Image Processing
NEW QUESTION # 16
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview
NEW QUESTION # 17
Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
Answer:
Explanation:
Decoder
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture
NEW QUESTION # 18
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
- A. FALSE
- B. TRUE
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models
NEW QUESTION # 19
The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.
- A. FALSE
- B. TRUE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models
NEW QUESTION # 20
The jieba ------() method can be used for word segmentation.
Answer:
Explanation:
cut
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Chinese Word Segmentation Tools
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. Perspective transformation
- B. Affine transformation
- C. Grayscale transformation
- D. Rotational transformation
Answer: B,D
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
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- B. The multi-head attention mechanism captures information about different subspaces within a sequence.
- C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
- D. The concatenated output is fed directly into the multi-headed attention mechanism.
Answer: B,C
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Multi-Head Attention
NEW QUESTION # 23
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Statistical Parameter Estimation
NEW QUESTION # 24
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation
NEW QUESTION # 25
Which of the following statements about the functions of the encoder and decoder is true?
- A. The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
- B. The encoder converts context vectors into variable-length output sequences.
- C. The output lengths of the encoder and decoder are the same.
- D. The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
Answer: D
Explanation:
In anencoder-decoderarchitecture:
* Theencoderprocesses variable-length inputs and encodes them into fixed-length context vectors that summarize the input. (Cis correct.)
* Thedecodergenerates output sequences from this context, which may be of variable length.
* Adescribes the decoder incorrectly;Bmixes roles;Dis false because output length depends on the target sequence, not the encoder output length.
Exact Extract from HCIP-AI EI Developer V2.5:
"The encoder transforms variable-length sequences into context vectors, which the decoder uses to generate variable-length outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Functions
NEW QUESTION # 26
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
- A. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
- B. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
- C. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
- D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
Answer: A,C,D
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Evaluation
NEW QUESTION # 27
Which of the following is not an acoustic feature of speech?
- A. Duration
- B. Amplitude
- C. Frequency
- D. Semantics
Answer: D
Explanation:
In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such as duration(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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 28
------- is a model that uses a convolutional neural network (CNN) to classify texts.
Answer:
Explanation:
Text CNN
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN Applications in NLP
NEW QUESTION # 29
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer vs. RNN Performance
NEW QUESTION # 30
Which of the following are object detection algorithms?
- A. YOLO
- B. SSD
- C. R-CNN
- D. Faster-R-CNN
Answer: A,B,C,D
Explanation:
The major families of object detection algorithms include:
* R-CNN (Region-based CNN):Uses region proposals with CNN feature extraction.
* YOLO (You Only Look Once):Performs real-time detection by predicting bounding boxes and class probabilities in a single pass.
* SSD (Single Shot MultiBox Detector):Uses multiple feature maps for detecting objects at different scales in one pass.
* Faster-R-CNN:Improves R-CNN with a Region Proposal Network for speed.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common object detection algorithms include R-CNN, Faster R-CNN, YOLO, and SSD, each using different approaches for balancing accuracy and speed." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection
NEW QUESTION # 31
......
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