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

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."

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."

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."

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."

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."

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."

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."

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."

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."

MFCCs are a widely used feature extraction method in speech recognition. The process involves:

Converting the time-domain signal to the frequency domain using the Fourier transform.

Mapping the frequency scale to theMel scaleto mimic human hearing perception.

Taking the logarithm of the power spectrum to emphasize perceptually important differences.

Applying the discrete cosine transform (DCT) to obtaincepstral coefficients.

These steps capture the spectral envelope, which is important for distinguishing phonemes in speech.

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

"MFCCs transform audio to the Mel scale, applying log compression and cepstral transformation to align with human auditory characteristics."

The convolutional layer in CNNs is optimized for spatial feature extraction:

Local connectivity(A) reduces computation and memory usage.

Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.

Stride control(C) allows adjusting the output resolution and computational cost.

Sliding kernel operation(D) extracts local patterns without manual feature definition.

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

"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers."

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."

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."

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."

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."