Forward Deployed Engineer Test

You are working on a large-scale, distributed, and fault-tolerant message processing system designed to handle high throughput and low latency requirements. The system is based on the publish-subscribe pattern and uses multiple brokers to distribute messages across various topics and partitions. In this architecture, both publishers and subscribers are considered clients. The brokers are responsible for replicating messages among themselves to ensure fault tolerance and data durability.

A client reports successfully publishing a message on a specific topic. However, one of the subscribers has not received the message. To investigate the issue, you have gathered detailed logs and system design data, as shown below:

Based on the information provided, which of the following is the most likely reason for the issue?

A: The message was not published on the topic
B: Client C is not subscribed to the correct topic
C: There is a replication lag between brokers B1 and B2
D: Client C is consuming from the wrong broker
E: The message processing system failed to acknowledge the message

A backend service is experiencing intermittent latency spikes while processing incoming requests. The service is deployed in a multi-node environment with a load balancer in front. You suspect that the issue might be related to resource contention. You collect the following performance metrics from the affected nodes during a spike:

Which of the following is the most probable cause of the latency spikes?
A: High memory usage on the affected nodes.
B: Disk I/O bottlenecks on the affected nodes.
C: Insufficient CPU resources on the affected nodes.
D: Uneven distribution of incoming requests by the load balancer.
E: Network latency between the load balancer and the backend nodes.

Consider a distributed e-commerce platform designed to handle high traffic volumes and ensure data consistency across its services. The platform uses a distributed database that replicates data across multiple nodes to increase availability and performance. To balance the load, it employs a load balancer that distributes user requests evenly across these nodes. The system is designed to tolerate the failure of up to two nodes without affecting the platform's overall availability.

Given the critical requirement for strong consistency to prevent issues such as overselling of products, the system uses a consensus algorithm for replication. The database is configured with a replication factor of 5, meaning each piece of data is stored on 5 nodes. For read and write operations to be considered successful, they must be acknowledged by a majority of the nodes involved in the operation.

Assuming all nodes have equal hardware resources and network latency between nodes is negligible, which of the following configurations would best meet the platform's requirements for high availability, performance, and strong consistency?

A: Reads require acknowledgment from 2 nodes, and writes require acknowledgment from 4 nodes.
B: Reads and writes both require acknowledgment from 3 nodes.
C: Reads require acknowledgment from 3 nodes, and writes require acknowledgment from 2 nodes.
D: Reads and writes both require acknowledgment from 4 nodes.
E: Reads require acknowledgment from 1 node, and writes require acknowledgment from 5 nodes.
F: Reads and writes both require acknowledgment from 5 nodes.

TransitTrack is a logistics company that needs to store real-time location data (latitude, longitude) of their vehicles as they move across the city. The system should be optimized for fast read and write operations to provide real-time tracking. TransitTrack can tolerate occasional data loss since the vehicle locations are updated frequently. Which of the following data storage solutions should TransitTrack implement for their vehicle tracking system?

A: Utilize a relational database management system (RDBMS) like PostgreSQL with a table indexed on the vehicle_id column for efficient data insertion and retrieval.
B: Implement an in-memory cache like Redis to store the vehicle location data, with the vehicle_id as the key and the latitude-longitude pair as the value.
C: Use a document-oriented database like MongoDB to store the vehicle location data as GeoJSON documents, enabling geospatial querying capabilities.
D: Develop a custom in-memory data structure using a spatial indexing technique like an R-tree to store and query the vehicle location data efficiently.
E: Use a time-series database like InfluxDB to store the vehicle location data along with timestamps, allowing for efficient querying and analysis of historical location data.

Johnny Bravo is setting up a new e-commerce store for men's clothing. He set up session stickiness with ELB. But he does not want ELB to manage the cookie, he wants the application to manage the cookie. When the server instance, which is bound to a cookie, crashes what do you expect will happen?

A: ELB will throw an error due to cookie unavailability
B: The response will have a cookie but stickiness will be deleted
C: The session will be sticky and ELB will route requests to another server as ELB keeps replicating the Cookie
D: The session will not be sticky until a new cookie is inserted

Imagine you’re a developer at Songbird Inc, working on a music editing app for mobile devices. The app allows users to edit audio clips and export them in various audio formats. Once a user finishes editing a clip, they can choose an output format and initiate the encoding process. This encoding process can take a while depending on the chosen format and the length of the clip. Because it’s a mobile app, you want to avoid freezing the UI while encoding is in progress.

What’s the most appropriate approach to notify the user when the encoding is complete and the exported file is ready?

A: Directly modify the UI elements from within the encoding logic. When encoding finishes, the encoding system can directly tell the UI components to update themselves with the new information (e.g., change a button text to “Export Complete”).
B: Separate the UI update logic from the encoding process. The encoding system should trigger a custom event (e.g., “EncodingFinishedEvent”) upon completion. UI components can listen for this event and update themselves accordingly when it’s received.
C:  Have the UI code continuously check on the encoding status with a loop (often referred to as busy waiting or polling). The loop would keep checking a flag or variable set by the encoding system until the encoding is complete. Once complete, the UI can update itself.
D:  Introduce a central message queue or event bus. The encoding system can publish a message to the message queue upon finishing the task. Separate UI update logic would be subscribed to the queue, listening for relevant messages. When it receives the message about encoding completion, it can update the UI.
E: Let the encoding logic return a callback function to the UI layer when it’s initiated. Once encoding is finished, the encoding system calls back this function, allowing the UI to update itself.