xAI Software Engineer System Design Questions

As xAI expands its machine learning capabilities, we need a robust API to manage model training, predictions, and evaluations. This API should handle requests efficiently and return results in a unified format.
Problem Statement: Design an API that allows users to submit training data, request model training, and retrieve predictions. The API should support the following operations: POST /train, GET /predict, and GET /status. Ensure to consider versioning and error handling in your design.
Example Operations:
- POST /train: Accepts a JSON payload with data and returns a 201 status with a task ID.
- GET /predict: Accepts a task ID and returns predictions based on trained data.
- GET /status: Checks the status of the training job based on task ID.
Constraints:
- Ensure the API can handle a minimum of 1000 concurrent requests with low latency.
- Should support flexible data formats (CSV, JSON).

To enhance performance for xAI's machine learning algorithms, we need a fast, in-memory caching system to store frequently accessed data. This system should allow for efficient data retrieval and support for automatic eviction of old data.
Problem Statement: Design a custom cache that supports the following operations: put(key: int, value: int): void and get(key: int): int. When the cache reaches its capacity, it should invalidate the least recently used (LRU) item.
- class LRUCache:
- def __init__(self, capacity: int): Initializes the cache with a positive size capacity.
- def get(self, key: int) -> int: Returns the value of the key if the key exists, otherwise returns -1.
- def put(self, key: int, value: int) -> None: Updates or adds the value if the key is not present. When the cache reaches its capacity, it should invalidate the least recently used item before adding the new item.
Example 1:
Input:
LRUCache cache = new LRUCache(2);
cache.put(1, 1);
cache.put(2, 2);
cache.get(1); // returns 1
cache.put(3, 3); // evicts key 2
cache.get(2); // returns -1 (not found)
Example 2:
Input:
LRUCache cache = new LRUCache(1);
cache.put(1, 1);
cache.get(1); // returns 1
cache.put(2, 2); // evicts key 1
cache.get(1); // returns -1 (not found)
Constraints:
- 1 <= capacity <= 3000
- 0 <= key <= 10000
- 0 <= value <= 10^9.

To enhance the user experience, xAI seeks to implement a robust tracking system for user engagement metrics across all its platforms.
Design an API to handle various types of engagement tracking with methods to log, retrieve, and aggregate user interactions.Method signatures:
- trackEvent(userId: int, eventType: str, timestamp: str) -> None: Logs an engagement event for a user at a specific timestamp.
- getUserEngagement(userId: int) -> List[str]: Returns a list of event types the user engaged in.
- aggregateEvents(eventType: str) -> int: Returns the count of how many times an event type has occurred across all users.Example Success Criteria:
- The system must handle at least 10 million user interactions per day.
- Ensure data consistency and integrity in logging system.
- Provide efficient query responses with minimal latency.Constraints:
- All eventType values will be unique strings.

To accelerate response times from our AI models, xAI needs to implement a caching mechanism for the frequently accessed data.
Design and implement a simple LRU Cache class that supports the following operations:
- get(key: int) -> int: Retrieve the value of the key if the key exists in the cache, otherwise return -1.
- put(key: int, value: int) -> None: Update the value of the key if the key exists. If the key does not exist, add the key-value pair to the cache. If the cache reaches its capacity, it should invalidate the least recently used item before inserting a new item.Example Method Signatures:
- def get(self, key: int) -> int
- def put(self, key: int, value: int) -> NoneExample 1:
Input: cache = LRUCache(2), cache.put(1, 1), cache.put(2, 2), cache.get(1)
Output: 1 Example 2:
Input: cache.put(3, 3), cache.get(2)
Output: -1
Explanation: The least recently used key (2) was removed because the cache reached its capacity.Constraints:
- capacity is at most 3000.