Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A logistics company wants to use AI to automatically sort packages based on their destination address printed on the label. Which AI workload combination is needed?
⚠ Common exam trap
Candidates often confuse OCR with general computer vision or assume a single workload (e.g., only OCR) suffices, ignoring that the output must be classified into a routing decision, which requires a separate classification model.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
OCR to read the printed address and classification to determine routing
The scenario requires two distinct AI workloads: OCR (Optical Character Recognition) to extract the printed destination address from the package label, and classification to map that address to the correct routing category (e.g., zip code, region, or delivery route). This combination directly solves the problem of reading unstructured text and assigning it to a predefined output class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reinforcement learning to optimize package sorting speed
Why it's wrong here
Reinforcement learning (RL) trains an agent to make sequential decisions by maximizing cumulative rewards through trial-and-error interactions with an environment. While RL could be used to optimize conveyor-belt speeds or robot arm trajectories in a sorting facility, it does not read or interpret address text on labels. The task described is a one-step perception-and-classification problem (read address, determine destination), not a sequential policy optimization problem with delayed rewards.
- ✓
OCR to read the printed address and classification to determine routing
Why this is correct
OCR (Optical Character Recognition) extracts the printed or handwritten address text from the package label image, converting it into machine-readable characters. A classification model (or rule-based logic) then maps those address components—such as postal code, city, or street—to the correct sorting destination or delivery route. This is the classic document-intelligence pipeline: extract text first, then interpret/structure it for downstream automation.
- ✗
Facial recognition to identify the delivery person
Why it's wrong here
Facial recognition is a biometric computer vision technique that identifies or verifies a specific person by analyzing facial features (e.g., face landmarks, embeddings). While it could be used to authenticate a delivery person at the door, it provides no information about the package's destination address or sorting bin. The task here is text extraction and address-based routing, so facial recognition is irrelevant to the core processing pipeline.
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Sentiment analysis to assess package condition
Why it's wrong here
Sentiment analysis is a natural language processing (NLP) technique that detects emotional tone (positive, negative, neutral) in written text, typically from reviews, social media, or feedback. Package condition is a physical, non-textual attribute; there is no emotional or opinion-bearing text on a shipping label to analyze. Sorting packages by address requires reading the printed text (OCR) and then classifying or matching that text to a destination, not assessing sentiment.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
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