A team is developing a facial recognition system for a security application. They have a dataset of frontal face images but need the system to work when people are looking slightly to the side. Which data augmentation technique is most appropriate?

Apply small random rotations and horizontal flips to the face images

Apply heavy color jitter to the face images

Apply aggressive random cropping to the face images

Use random erasing on the face images

A logistics company processes shipping labels that contain both printed text and handwritten recipient names. The labels are photographed at varying angles and under inconsistent lighting. The team needs to extract the printed tracking number and the handwritten name accurately. Which OCR approach is most appropriate?

Use a statistical OCR engine for the tracking number and template matching for the handwritten name

Use a neural-network-based recognizer for both fields because it handles all text types uniformly

Use a template-matching OCR engine for the tracking number and a neural-network-based recognizer for the handwritten name

Apply binarization to the whole image and use a single template-matching engine for both fields

A robotics company is building a system for a drone to follow a moving target. The target moves at varying speeds and can be briefly occluded by trees. The drone has a limited onboard computer. Which tracking approach is most suitable?

Optical flow tracking of the entire scene

Template matching in each frame

Kalman filter with an adaptive motion model and detection updates when the target is visible

Mean shift tracking with a fixed color histogram

Which object detection algorithm is known for its real-time processing speed by dividing the image into a grid and predicting bounding boxes and class probabilities directly?

SSD (Single Shot MultiBox Detector)

What is the primary purpose of data augmentation in computer vision?

To artificially increase the size and diversity of the training dataset

To compress images for storage efficiency

To reduce the resolution of images to speed up training

To label images manually for supervised learning

CompTIADataX

Domain 5Objective 3

Computer Vision DY0-001 Practice Questions (Page 3)

Part of the Specialized applications of data science domain, which accounts for 13% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~8–14 in this domain), expect 2–4 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

8concepts

13%of the exam

Questions 11–15

11application · medium
A team is developing a facial recognition system for a security application. They have a dataset of frontal face images but need the system to work when people are looking slightly to the side. Which data augmentation technique is most appropriate?
Select an answer first
12application · medium
A logistics company processes shipping labels that contain both printed text and handwritten recipient names. The labels are photographed at varying angles and under inconsistent lighting. The team needs to extract the printed tracking number and the handwritten name accurately. Which OCR approach is most appropriate?
Select an answer first
13expert · hard
A robotics company is building a system for a drone to follow a moving target. The target moves at varying speeds and can be briefly occluded by trees. The drone has a limited onboard computer. Which tracking approach is most suitable?
Select an answer first
14foundation · easy
Which object detection algorithm is known for its real-time processing speed by dividing the image into a grid and predicting bounding boxes and class probabilities directly?
Select an answer first
15foundation · easy
What is the primary purpose of data augmentation in computer vision?
Select an answer first

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