Parse logs and generate NFT attributes
You are presented with two independent coding tasks.
Task 1: Log File Parsing and Grouping
You will receive a list of log lines, each represented as a string. Each log line is guaranteed to contain at least:
- A timestamp, which is parseable, comparable, and can be assumed to follow ISO-8601 or an integer epoch format.
- A thread ID, which can be an integer or a string.
- An arbitrary message content.
An example log line format is: 2026-02-13T10:15:30Z thread=42 msg=Starting work. You may assume a consistent format across all provided log lines.
Requirements
- Parse each individual log line to extract its
(timestamp, threadId, message)components. - Group all parsed log entries by their respective
threadId. - For each unique
threadId, sort its associated log entries in ascending order based on theirtimestamp. - The final output should be a data structure, such as
Map<threadId, List<LogEntry>>(whereLogEntryis a custom object containing the parsed components) orMap<threadId, List<String>>(preserving the original log lines), with each list guaranteed to be time-sorted.
Edge Cases
- Duplicate Timestamps: Handle scenarios where multiple log entries within the same thread might share an identical timestamp.
- Malformed Lines: Define and apply a reasonable policy for handling log lines that do not conform to the expected format (e.g., skip them, throw an error, or collect them for later review).
Task 2: NFT Attribute Combination Generation
You are given a collection of NFT attributes, structured such that each attribute category has a list of its possible values.
Example input format:
Background: ["Red", "Blue"]Ears: ["Pointy", "Wide"]Hat: ["Cap", "None"]
Part A: Enumerate All Combinations
Generate all possible unique NFTs by selecting exactly one value from each attribute category. The result should be a list of these unique combinations (effectively, the Cartesian product of the attribute values).
Follow-up 1: Deduplication
Consider scenarios where the input might contain duplicate values within an attribute category (e.g., Ears: ["Pointy", "Pointy", "Wide"]), or where different paths through the attribute selection process could lead to an identical final combination. Ensure that the final output list contains only unique NFT combinations.
Follow-up 2: Weighted/Random Generation
Now, assume that each attribute value is associated with a specific weight or probability (these are not necessarily uniform).
Example with weights:
Ears: Pointy=0.6, Wide=0.4
Design and explain an algorithm to randomly generate a single NFT combination according to these specified weights for each attribute category. Detail how you would implement the efficient sampling step. Additionally, discuss how you would handle avoiding duplicate NFTs if the requirement was to generate a large number of unique NFTs using this weighted random approach.
AI interview prep
Core dilemma
The ability to demonstrate proficiency across distinct problem domains: efficient data parsing and transformation (Task 1) versus robust combinatorial generation and probabilistic sampling (Task 2), while maintaining code quality and handling edge cases.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Parse logs and generate NFT attributes
You are presented with two independent coding tasks.
Task 1: Log File Parsing and Grouping
You will receive a list of log lines, each represented as a string. Each log line is guaranteed to contain at least:
- A timestamp, which is parseable, comparable, and can be assumed to follow ISO-8601 or an integer epoch format.
- A thread ID, which can be an integer or a string.
- An arbitrary message content.
An example log line format is: 2026-02-13T10:15:30Z thread=42 msg=Starting work. You may assume a consistent format across all provided log lines.
Requirements
- Parse each individual log line to extract its
(timestamp, threadId, message)components. - Group all parsed log entries by their respective
threadId. - For each unique
threadId, sort its associated log entries in ascending order based on theirtimestamp. - The final output should be a data structure, such as
Map<threadId, List<LogEntry>>(whereLogEntryis a custom object containing the parsed components) orMap<threadId, List<String>>(preserving the original log lines), with each list guaranteed to be time-sorted.
Edge Cases
- Duplicate Timestamps: Handle scenarios where multiple log entries within the same thread might share an identical timestamp.
- Malformed Lines: Define and apply a reasonable policy for handling log lines that do not conform to the expected format (e.g., skip them, throw an error, or collect them for later review).
Task 2: NFT Attribute Combination Generation
You are given a collection of NFT attributes, structured such that each attribute category has a list of its possible values.
Example input format:
Background: ["Red", "Blue"]Ears: ["Pointy", "Wide"]Hat: ["Cap", "None"]
Part A: Enumerate All Combinations
Generate all possible unique NFTs by selecting exactly one value from each attribute category. The result should be a list of these unique combinations (effectively, the Cartesian product of the attribute values).
Follow-up 1: Deduplication
Consider scenarios where the input might contain duplicate values within an attribute category (e.g., Ears: ["Pointy", "Pointy", "Wide"]), or where different paths through the attribute selection process could lead to an identical final combination. Ensure that the final output list contains only unique NFT combinations.
Follow-up 2: Weighted/Random Generation
Now, assume that each attribute value is associated with a specific weight or probability (these are not necessarily uniform).
Example with weights:
Ears: Pointy=0.6, Wide=0.4
Design and explain an algorithm to randomly generate a single NFT combination according to these specified weights for each attribute category. Detail how you would implement the efficient sampling step. Additionally, discuss how you would handle avoiding duplicate NFTs if the requirement was to generate a large number of unique NFTs using this weighted random approach.