H13-321_V2.5 HCIP - AI EI Developer V2.5 Exam Questions and Answers

In feature selection for classification tasks, thechi-square (χ²)statistical test can be applied to evaluate the independence between features and target labels.

In Python’s scikit-learn library, this is implemented using:

python

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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."

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."

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."

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."

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."

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."

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."

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."

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."

Deep learning–based speech recognition offers two key advantages over traditional approaches:

Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.

End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.

Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.

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

"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs."

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."

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."

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."

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."