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Practice Test: Computer Vision & Recommendation Systems

A combined 100-mark paper covering "Computer Vision: How Machines Learn to See" and "Recommendation Systems: How Netflix and Spotify \u2018Know\u2019 What You Like" — multiple choice, true/false, fill-in-the-blank, column matching, and short answer, all auto-graded instantly.

EDUSAMBAM Editorial Team · 45 questions · 100 marks · Instant self-assessment

Questions

100

Marks

Sections

100%

Auto-Graded

Section	Questions	Marks Each	Total
A — Multiple Choice	15	2	30
B — True / False	10	2	20
C — Fill in the Blank	10	2	20
D — Column Matching	5 pairs	2	10
E — Short Answer	5	4	20
Total	45	—	100

Fill-in-the-blank and short-answer questions are checked automatically against accepted answers — exact wording doesn't need to match, but the key term should be present.

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Section A — Multiple Choice

30 marks

15 questions · 2 marks each.

1.What is computer vision?Basic2 pts

A. A type of computer monitor

B. The field of AI that enables computers to interpret and understand visual information

C. A brand of camera lens

D. Software for editing spreadsheets

2.What is "object detection" in computer vision?Basic2 pts

A. Deleting unwanted files from a folder

B. Repairing a broken camera

C. Identifying and locating specific objects within an image

D. Compressing an image file

3.How does computer vision typically analyse an image?Basic2 pts

A. By examining pixels and learned patterns to recognise features

B. By printing the image and reading it manually

C. By asking a human to describe it every time

D. By ignoring the image content entirely

4.What is a convolutional neural network (CNN) commonly used for?Basic2 pts

A. Sending emails

B. Processing and analysing image data

C. Formatting text documents

D. Managing a computer's power supply

5.Which of these is a real-world application of computer vision?Basic2 pts

A. A basic text editor

B. A plain calculator app

C. A USB flash drive

D. A self-driving car recognising road signs and pedestrians

6.What is facial recognition?Basic2 pts

A. A method of encrypting files

B. A way to compress video

C. Identifying a person's identity from an image of their face

D. A type of computer password

7.What can make computer vision less accurate?Basic2 pts

A. Poor lighting or unusual camera angles

B. Using a faster internet connection

C. Having too much storage space

D. Using a newer keyboard

8.What is a recommendation system?Basic2 pts

A. A system that repairs broken software

B. A system that suggests content or products based on user data and preferences

C. A type of computer virus

D. A tool for managing passwords

9.What do recommendation systems typically base their suggestions on?Basic2 pts

A. Random selection with no data involved

B. The weather forecast

C. A user's past behaviour, preferences, and activity

D. The device's battery level

10.What is "collaborative filtering"?Basic2 pts

A. Recommending items based on what similar users liked

B. Deleting duplicate files

C. Sorting files by date only

D. Blocking spam emails

11.What is "content-based filtering"?Basic2 pts

A. Blocking inappropriate content automatically

B. Recommending items similar to what a user has liked before

C. Filtering out low-quality images

D. Removing viruses from a file

12.Which platforms commonly use recommendation systems?Basic2 pts

A. A basic calculator app

B. A plain text file viewer

C. A basic alarm clock app

D. Streaming services like Netflix and Spotify

13.Which type of data do recommendation systems typically use?Basic2 pts

A. Watch history, ratings, and past purchases

B. Only the current date and time

C. Only the device's screen brightness

D. The colour of the app icon

14.What is the main purpose of a recommendation system?Basic2 pts

A. To slow down the user's browsing

B. To personalise suggestions and help users discover relevant content

C. To delete a user's account

D. To increase a device's battery life

15.Which of these best describes how computer vision and recommendation systems are similar?Basic2 pts

A. They are exactly the same technology

B. Neither uses any data at all

C. Both are AI applications that learn patterns from data to make useful predictions

D. Both only work without an internet connection

Section B — True / False

20 marks

10 questions · 2 marks each.

16.Computer vision enables computers to interpret and understand visual information from images and video.Intermediate2 pts

True

False

17.Computer vision cannot be used in self-driving cars.Intermediate2 pts

True

False

18.Convolutional neural networks (CNNs) are commonly used for processing image data.Intermediate2 pts

True

False

19.Facial recognition is completely unrelated to computer vision.Intermediate2 pts

True

False

20.Recommendation systems suggest content based on a user's data and behaviour.Intermediate2 pts

True

False

21.Collaborative filtering recommends items based on what similar users liked.Intermediate2 pts

True

False

22.Content-based filtering completely ignores what a user has liked in the past.Intermediate2 pts

True

False

23.Netflix and Spotify both use recommendation systems to personalise suggestions.Intermediate2 pts

True

False

24.Lighting and camera angle can affect the accuracy of computer vision systems.Intermediate2 pts

True

False

25.Recommendation systems never use a user's past behaviour or history.Intermediate2 pts

True

False

Section C — Fill in the Blank

20 marks

10 questions · 2 marks each. Type the missing word or term.

26.______ vision is the field of AI that enables computers to interpret and understand visual information.Intermediate2 pts

27.______ detection identifies and locates specific objects within an image.Intermediate2 pts

28.______ recognition identifies a person's identity from an image of their face.Intermediate2 pts

29.A ______ neural network (CNN) is commonly used to process image data.Intermediate2 pts

30.A ______ system suggests content or products based on user data and preferences.Intermediate2 pts

31.______ filtering recommends items based on what similar users liked.Intermediate2 pts

32.______-based filtering recommends items similar to what a user has liked before.Intermediate2 pts

33.Recommendation systems analyse past ______ such as watch history and ratings.Intermediate2 pts

34.Streaming platforms such as Netflix use recommendation systems to suggest ______.Intermediate2 pts

35.Factors such as lighting and camera ______ can affect computer vision accuracy.Intermediate2 pts

Section D — Column Matching

10 marks

Use the ▲ ▼ buttons to reorder Column B until each row lines up with the correct term in Column A. 2 marks per correct pair.

36–40.Match each Computer Vision/Recommendation Systems term to its definition.Advanced10 pts

Column A

Computer Vision

Object Detection

Facial Recognition

Collaborative Filtering

Content-Based Filtering

Column B

Identifying and locating specific objects within an image

Identifying a person's identity from an image of their face

Recommending items based on what similar users liked

Recommending items similar to what a user has liked before

The field of AI that enables computers to interpret visual information from images and video

Section E — Short Answer

20 marks

5 questions · 4 marks each. Answer in one or two sentences.

41.What is computer vision?Advanced4 pts

42.Give an example of a real-world application of computer vision.Advanced4 pts

43.What is the key difference between collaborative and content-based filtering?Advanced4 pts

44.Give an example of a platform that uses a recommendation system.Advanced4 pts

45.What kind of data do recommendation systems typically use?Advanced4 pts

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