[Feb-2026] Get 100% Real Free HCIP-AI EI Developer H13-321_V2.5 Sample Questions
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NEW QUESTION # 27
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
- A. TRUE
- B. FALSE
Answer: A
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 # 28
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
ModelArts is built to support a variety of compute resources, including CPUs, GPUs, and Ascend AI processors. This heterogeneous resource pool allows users to select the hardware that best matches their training or inference requirements, ensuring cost efficiency and optimal performance for different workloads.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts supports heterogeneous compute environments, enabling selection among CPUs, GPUs, and Ascend processors for flexible AI development." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Infrastructure
NEW QUESTION # 29
Which of the following statements about the levels of natural language understanding are true?
- A. Semantic analysis is to analyze the structure of sentences and phrases to find out the relationship between words and phrases, as well as their functions in sentences.
- B. Pragmatic analysis is to study the influence of the language's external environment on the language users.
- C. Syntactic analysis is to find out the meaning of words, structural meaning, their combined meaning, so as to determine the true meaning or concept expressed by a language.
- D. Lexical analysis is to find the lexemes of a word and obtain linguistic information from them.
- E. Speech analysis involves distinguishing independent phonemes from a speech stream based on phoneme rules, and then identifying syllables and their lexemes or words according to the phoneme form rules.
Answer: B,D,E
Explanation:
* 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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Levels of Language Understanding
NEW QUESTION # 30
Which of the following are required for the image object detection algorithm?
- A. Confidence calculation
- B. Object classification determination
- C. Object location calculation
- D. Object contour calculation
Answer: A,B,C
Explanation:
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."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow
NEW QUESTION # 31
If OpenCV is used to read an image and save it to variable "img" during image preprocessing, (h, w) = img.
shape[:2] can be used to obtain the image size.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Reading and Writing with OpenCV
NEW QUESTION # 32
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
- A. TRUE
- B. FALSE
Answer: A
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 # 33
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. Grayscale transformation
- B. Affine transformation
- C. Rotational transformation
- D. Perspective transformation
Answer: B,C
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 # 34
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Answer:
Explanation:
NER
Explanation:
NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks
NEW QUESTION # 35
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 concatenated output is fed directly into the multi-headed attention mechanism.
- D. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
Answer: B,D
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 # 36
In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
Answer:
Explanation:
3
Explanation:
In OpenCV:
* cv2.imread(filename, 1) reads the image incolor mode.
* This loads the image as a3-channelBGR image (Blue, Green, Red).
* Other modes: 0 for grayscale, -1 for unchanged (including alpha channel).
Exact Extract from HCIP-AI EI Developer V2.5:
"When the second parameter of cv2.imread is 1, the image is read in color mode, resulting in a 3-channel BGR image." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Reading and Writing with OpenCV
NEW QUESTION # 37
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
- A. ReLU
- B. Sigmoid
- C. SeLU
- D. Tanh
Answer: B
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 # 38
-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)
Answer:
Explanation:
FastText
Explanation:
FastTextis an extension of Word2Vec developed by Facebook AI Research. Unlike Word2Vec, which learns embeddings for whole words, FastText represents each word as a sum of its character n-gram embeddings.
This helps in handling rare words and morphologically rich languages by generating embeddings for unseen words from their subword components.
Exact Extract from HCIP-AI EI Developer V2.5:
"FastText decomposes words into character n-grams and represents words as the sum of their n-gram vectors, improving representation for rare and out-of-vocabulary words." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Subword Embedding Models
NEW QUESTION # 39
John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?
- A. Output delay
- B. Model development framework
- C. Demand for computing power
- D. Model security
Answer: B
Explanation:
When deploying a pre-trained large model locally for a specific function, themodel development framework used 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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Deployment Considerations
NEW QUESTION # 40
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
- A. TRUE
- B. FALSE
Answer: A
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 # 41
------- 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 # 42
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
- A. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
- B. Color image input is multi-channel, whereas text input is single-channel.
- C. CNNs are suitable for image tasks, but they perform poorly in text tasks.
- D. For CNN, there is no difference in handling text or image tasks.
Answer: A,B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP
NEW QUESTION # 43
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