Revolut Interview Questions (6+ Questions)

Users are complaining about slow file access and we have high disc utilization. We need to reduce IO activity of top offenders using IO priorities and we need to settle the IO priority to idle. While doing so, we need to keep critical jobs, databases, message queues, applications at high priority. First we need to identify processes that have high IO activity. We'll use IO top command, and we at hyphen N 10 at the end of this command. This means that this command will run 10 times. To check current IO priority of job one, we need to use ionice command. First we need to look into process ID of this job. We have no priority set for this process, and we need to set this to idle. And idle will be priority number three. The command for this will be ionice three and the process ID.

Kubernetes StatefulSet Storage Persistence & Re-attachment: app StatefulSet. Validate data durability by simulating a Pod failure and confirming that the replacement replica automatically inherits the existing PersistentVolumeClaim (PVC). Challenge demonstrates the critical decoupling of compute lifecycle from storage lifecycle, ensuring zero data loss and seamless volume re-binding during Pod recreation or node rescheduling.

Kubernetes Headless Service DNS Discovery: discovery-svc Multiple A Records disco. Fix peer discovery failures for discovery-app clustering (3 replicas, app=discovery) in disco namespace. Convert discovery-svc from ClusterIP (single IP) to headless service (clusterIP: None) returning one A record per pod IP via DNS discovery-svc.disco.svc.cluster.local. Perfect for stateful clustering, etcd/consul discovery, service mesh peer awareness, P2P applications, database replication, and distributed system bootstrapping.

Spark is a framework that processes large amounts of data across multiple machines. Instead of tables, it uses data frames. We have only one file that is called background_checks.csv. Four of these columns store multiple values in one string separated by comma. We are required to count how many items are there in each of those strings and create four new columns to store these results. Everything should be sorted out in ascending order by ID. We read the CSV file into data frame using given path. When header is set to true, it uses first row of the data frame as column names, and inferSchema automatically detects data types. We use split function that takes a string and cuts it into an array using a separator. We wrap everything inside of size function that will count how many elements are in the array. We sort everything out in ascending order by check ID using order by.

Objective

Write an SQL query to list each employee's name along with the total number of projects they are assigned to. Ensure that employees with no project assignments are also included in the result with a project count of zero. The final list should be ordered first by the number of projects...

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def is_happy(n: int) -> bool:
visit = set()

while n not in visit:
    visit.add(n)
    
    sq_sum = 0
    while n > 0:
        digit = n % 10
        sq_sum += digit * digit
        n //= 10
    n = sq_sum
    
    if n == 1:
        return True
        
return False