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Hello Amer,
Thank you for raising this.
For the first question, the answer set is correct. Here’s a brief explanation for each match:
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Time-series forecasting → seasonal sales forecasting
Time-series forecasting analyzes data points collected over time to predict future values. Seasonal sales forecasting is a classic example, as it uses historical trends and seasonality patterns to predict future sales. -
Computer vision → pattern recognition
Computer vision enables systems to interpret and analyze visual data (images or videos). Pattern recognition in visual inputs—such as identifying shapes, faces, or objects—is a core capability of computer vision. -
Natural language processing (NLP) → chatbot assistant
NLP allows machines to understand and generate human language. Chatbot assistants rely on NLP to interpret user input and respond conversationally. -
Classification → object detection
Classification involves assigning labels to data based on learned features. In object detection scenarios, models classify detected objects into categories (for example, car, person, dog).
For the second question about facial recognition and potential exclusion of certain demographics, the correct answer should be Fairness, since this principle focuses on identifying and mitigating bias across demographic groups.
That said, we will still review both questions internally and update them if needed to ensure clarity and accuracy.
We appreciate you flagging this—your feedback helps us continuously improve the content.
Kind regards,
Nikee @ Tutorials Dojo -
