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."

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."

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."

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."

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."