Microsoft Interview Tips: Real Questions Answers & OA Interviews

Title: Identify and filter fraudulent transactions in real-time for Microsoft's Azure billing system.

Non-functional needs:

• Hundreds of thousands of queries processed per second during peak periods
• High availability, no downtime even with regional failures
• Fault tolerance, the system should be able to gracefully handle the failure of a single component or data center
• Low latency, fraud detection should be accomplished in near real time within a few tens of milliseconds
• Accuracy, minimizing false alarms and missed alarms
• Cost-effective and efficient utilization of cloud resources

This is to examine the candidate's grasp of the overall architecture of the real-time fraud detection system, especially in the very high concurrency, high availability, low latency and accuracy of these points, the candidate immediately with the interviewer to confirm some details, such as the specific definition of "fraud", need to pay attention to what types of anomalous behavior (eg: Unusually large purchases, multiple transactions across geographies in a short period of time, unusual resource creation behavior, etc.). Then I followed the interviewer's lead and indicated the direction of the discussion, whether I wanted to talk about the detailed design of the API and data model first, or build the high-level architecture first, and the discussion points were roughly:

System Design Discussion.

Do you want me to discuss the data models for transactions and user profiles, and the API for submitting transactions? (您是想我详细讨论交易和用户資料的数据模型以及交易提交的 API for submitting transactions?)

Or do you want me to discuss the high-level infrastructure components for real-time fraud detection first?

The interviewer said to start with the high-level components first, requiring candidates to work around the points of data inflow (Event Hub/Kafka), real-time processing engine (Stream Analytics/Flink/Spark Streaming), rules engine (CEP, Complex Event Processing), machine learning modeling services ( Azure Machine Learning/TensorFlow Serving), feature storage (Redis/Cosmos DB), and alerting and disposition (Function Apps/Logic Apps), while emphasizing how to ensure high availability and fault tolerance through multi-live architecture, data partitioning, asynchronous processing, and elastic scaling. availability and fault tolerance, and utilizing caching and in-memory computing to reduce latency. In terms of ensuring accuracy, it also mentioned how to combine historical data training models, real-time feature engineering, and manual review mechanisms. This part of the conversation feels okay, mainly to show the breadth and depth of your thinking, as well as the ability to trade-off under different needs.

The system design chat lasted about 20 minutes or so, then it was time for the algorithms session. The interviewer gave this question:

Coding Problem.

Given a stream of transaction data, design and implement a component that detects an abnormal spike in transaction counts for a specific user during a rolling time window. Specifically, if a user's transaction count in the past minute is the average of its transaction count in the past hour $N$ times or more, it is marked as abnormal.

This question is not particularly difficult, but there are a lot of details, the first reaction to the question is to ask about the structure of the input data (such as user ID, timestamp, amount, etc.), as well as the specific definition and parameters of the "abnormal peaks". $N$ value range, and the granularity of the time window (minutes, hours), the interviewer said to start with the assumption that the transaction data is streamed in JSON format containing the userId, timestamp, amount fields, the$N$ It can be any positive floating point number and the time window is accurate to the second.

Problem Solving Ideas:

First, we need to maintain two sliding windows for each user: one for transaction counts in a one-minute window, and another for transaction counts in an hourly window. This can be done using thehash table(or a similar structure) is implemented, where the key is a userId and the value is a queue or list containing a timestamp.

Specifics on realization:

1. Data structure selection:
   
   • For each user, we can use adouble-ended queue(collections.deque works well in Python) to store every transaction in the past minute and the past hour in thetimestampThe
   • Or, more efficiently, we could maintain two counters for each user: last_minute_count and last_hour_count, and combine them with a timestamped queue to dynamically adjust both counts. When a new transaction comes in, update the counter; when an old transaction expires, decrease the counter.
2. Sliding window logic:
   
   • When a new transaction is received, its timestamp is recorded.
   • Iterate through both queues (or timestamps) for each user, removing all timestampsOut of the current window(e.g., more than a minute or an hour) of transactions. The corresponding count is updated at the same time.
   • Add the timestamp of the new transaction to the queue and update both counts.
3. anomaly detection:
   
   • After each update of the count, the current user is counted in theWithin a one-minute windowNumber of transactions (C1min​) andWithin one hour windowThe average number of transactions (Avg1hr​=C1hr​/60).
   • in the event that C1min​>N×Avg1hr​, then the user is marked as an exception. Needs to be handled Avg1hr​ is zero to avoid dividing by zero errors, in which case you can set a minimum threshold, for example when C1min​ Exceeding a certain absolute value is also considered an exception.
4. make superior:
   
   • Considering that the number of users can be very large, directly storing a timestamped queue of all transactions for all users would take up a lot of memory. We can consider using a fixed-sizeSliding Window Arrayor better.Time series database(e.g., InfluxDB) to store and query data, but for interviews, an in-memory hash table plus a queue is a more straightforward solution.
   • If you are only concerned with counts and not specific transactions, you can use a more lightweight counter, such as updating the counts at fixed intervals instead of every transaction.
   • For real-time stream processing, you can use thewindow function(e.g. in Flink or Spark Streaming) to automatically manage sliding windows and counts.

When writing code on the spot, candidates need to try to make variable and function names as clear and understandable as possible, and explain the logic to the interviewer while writing, emphasizing theTreatment of boundary conditions(e.g., user's first transaction, insufficient number of transactions in the window to compute an average, etc.) It should be specifically mentioned that for efficiency reasons, we should not traverse the entire queue each time, but rather take advantage of the nature of double-ended queues to remove only the elements at the head of the queue that have expired when the window slides.