Data Abstraction Practice Test

Based on the scenario described, a Traffic Management System must coordinate multiple intersections. Raw sensor counts arrive per lane, but the system abstracts each intersection into a single object with fields: totalIncomingCars, dominantDirection, and congestionLevel. An algorithm then prioritizes intersections by congestionLevel and adjusts only the top three most congested intersections each cycle, keeping the rest on default timing to save computation.

Pseudocode:

ints <- MAP(allIntersections, SUMMARIZE)

hotspots <- TOP_K(ints, by = congestionLevel, k = 3)

FOR each h IN hotspots:

  ADJUST_SIGNALS(h, dominantDirection)

Considering the example provided, how does the system abstract data to improve efficiency?

Based on the scenario described, a Medical Database must support quick allergy checks during prescribing. Raw patient notes may contain many sentences, but the system abstracts allergies into a standardized list of entries with fields: substance, reaction, and severity (low/medium/high). When a doctor selects a medication, an algorithm compares the medication’s ingredient list to the patient’s abstracted allergy list and blocks the order if any high-severity match occurs.

Pseudocode:

FUNCTION canPrescribe(patientID, medID):

  allergies <- Patients<u>patientID</u>.AllergyList

  ingredients <- Meds<u>medID</u>.Ingredients

  RETURN NOT EXISTS(a IN allergies WHERE a.severity=="high" AND a.substance IN ingredients)

Considering the example provided, which abstraction technique is used in the scenario to simplify data handling?