AIF-C01 Question 10
Single answerA retail company uses a machine learning model to recommend products to customers on their website. They want to ensure that recommendations are made instantly as users browse their website. What type of inferencing should the company use in this scenario?
- A
Real-time inferencing
- B
Batch inferencing
- C
Offline inferencing
- D
Scheduled inferencing
Show answer and explanation
Correct answer: A
Explanation
The scenario requires instant recommendations as users browse the website. Real-time inferencing is the appropriate choice because it provides low-latency predictions, enabling the system to deliver recommendations immediately. Batch and scheduled inferencing are unsuitable due to their delayed nature, and 'offline inferencing' is not a relevant concept in this context.
- A. Correct.
Real-time inferencing is suitable when predictions are needed instantly or with minimal latency, such as providing product recommendations as users browse a website.
- B. Incorrect.
Batch inferencing processes multiple predictions at once, typically for non-time-sensitive tasks, making it unsuitable for real-time recommendations.
- C. Incorrect.
Offline inferencing is not a standard term in AWS services or AI practices, and does not apply to this scenario.
- D. Incorrect.
Scheduled inferencing involves running predictions at scheduled intervals, which would not meet the requirement for instant recommendations.