Data Operations and Big O | Dataford Interview Questions

Perform standard data operations (sort, filter, reduce) on a dataset and analyze the Big O complexity of your approach.

Implement process_dataset(records, minimum_score), where each record is a dictionary containing name and integer score. Return a dictionary with records whose scores meet the threshold, sorted by descending score, and the sum of their scores. For example, [{'name': 'A', 'score': 8}, {'name': 'B', 'score': 3}] with 5 returns {'records': [{'name': 'A', 'score': 8}], 'total_score': 8}. An empty result returns an empty list and total 0.

Constraints

0 <= len(records) <= 1000
Each record contains a string field 'name' and an integer field 'score'
0 <= record['score'] <= 10^9
0 <= minimum_score <= 10^9
Records with equal scores may appear in any relative order

Function Signature

def process_dataset(records, minimum_score):

Your question is Data Operations and Big O. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Perform standard data operations (sort, filter, reduce) on a dataset and analyze the Big O complexity of your approach.

Constraints

0 <= len(records) <= 1000
Each record contains a string field 'name' and an integer field 'score'
0 <= record['score'] <= 10^9
0 <= minimum_score <= 10^9
Records with equal scores may appear in any relative order

Function Signature

def process_dataset(records, minimum_score):

Your question is Data Operations and Big O. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.