"For Xbox, the next-generation product I propose is a virtual reality headset. The rationale is threefold:
Industry Trends: Competitors like Meta and Apple are investing heavily in VR/AR, signaling strong market potential.
Xbox Legacy: Xbox has a history of pushing innovation in gaming hardware (e.g., Kinect), and VR represents the next frontier beyond traditional consoles.
Strategic Need: With console sales flattening, Xbox must expand into new experiences that deepen engagement and different"
Anonymous Stork - "For Xbox, the next-generation product I propose is a virtual reality headset. The rationale is threefold:
Industry Trends: Competitors like Meta and Apple are investing heavily in VR/AR, signaling strong market potential.
Xbox Legacy: Xbox has a history of pushing innovation in gaming hardware (e.g., Kinect), and VR represents the next frontier beyond traditional consoles.
Strategic Need: With console sales flattening, Xbox must expand into new experiences that deepen engagement and different"See full answer
"\# An program that prints out the peak elements in a list of integers.
Pseudocode:
1. Define a function that takes a list of integers as input.
2. Initialize an empty list to store the peak elements.
3. Loop through the list of integers.
4. For each element, check if it is greater than its neighbors.
5. If it is, add it to the list of peak elements.
6. Return the list of peak elements.
def findpeakelements(nums):
if not nums:
return []
peaks = []
n = len(nums"
Frederick K. - "\# An program that prints out the peak elements in a list of integers.
Pseudocode:
1. Define a function that takes a list of integers as input.
2. Initialize an empty list to store the peak elements.
3. Loop through the list of integers.
4. For each element, check if it is greater than its neighbors.
5. If it is, add it to the list of peak elements.
6. Return the list of peak elements.
def findpeakelements(nums):
if not nums:
return []
peaks = []
n = len(nums"See full answer
"supervised learning: model is trained on the labeled data
unsupervised learning: no labels provided - model learns by finding patterns , structure and groupings in the data.
Semi-supervised learning: use small set of labels to guide learning for the larger pool of unlabeled data.
reinforcement learning: leans by interacting with students the environment, receives reward and penalties based on actions
self supervised: no labelled data . The model makes its own practice problems by"
Anchal V. - "supervised learning: model is trained on the labeled data
unsupervised learning: no labels provided - model learns by finding patterns , structure and groupings in the data.
Semi-supervised learning: use small set of labels to guide learning for the larger pool of unlabeled data.
reinforcement learning: leans by interacting with students the environment, receives reward and penalties based on actions
self supervised: no labelled data . The model makes its own practice problems by"See full answer
"I would meet with my team to discuss and break down the 12 features into sub-tasks based on priority, then arrange a meeting with stakeholders to align on the priority levels and secure their approval.Next, I’d assign each of the main Priority 1 features to engineers accordingly, ensuring that the first three months focus on P1 delivery. The following three months would be dedicated to testing the P1 features while progressing with lower-priority features in parallel. This ensures that by month"
Riley M. - "I would meet with my team to discuss and break down the 12 features into sub-tasks based on priority, then arrange a meeting with stakeholders to align on the priority levels and secure their approval.Next, I’d assign each of the main Priority 1 features to engineers accordingly, ensuring that the first three months focus on P1 delivery. The following three months would be dedicated to testing the P1 features while progressing with lower-priority features in parallel. This ensures that by month"See full answer
Technical Program Manager
Program Sense
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"Idea:
Time complexity: O(n) each node visited once
Space complexity: O(h) recursion stack
For each node, we want to verify:
Descendants here means all nodes in the subtree excluding the node itself.
We do a post-order traversal (process children before the node itself).
For leaf nodes, there are no descendants, so they are valid by definition.
For internal nodes:
Recursively compute the sum and count of nodes in the left and right subtrees.
Calculate the total sum and"
Arya C. - "Idea:
Time complexity: O(n) each node visited once
Space complexity: O(h) recursion stack
For each node, we want to verify:
Descendants here means all nodes in the subtree excluding the node itself.
We do a post-order traversal (process children before the node itself).
For leaf nodes, there are no descendants, so they are valid by definition.
For internal nodes:
Recursively compute the sum and count of nodes in the left and right subtrees.
Calculate the total sum and"See full answer
"Constraints: 4-direction moves; no mode switching (pick exactly one of {1=bicycle, 2=bike, 3=car, 4=bus} for the full trip).
Per-mode search:
If a mode’s per-step time/cost are uniform, run BFS on allowed cells. Then totaltime = steps × timeperstep, tie-break by steps × costper_step.
If time/cost vary by cell (given matrices), run Dijkstra per mode minimizing (totaltime, totalcost) lexicographically. Maintain the best ⟨time, cost⟩ per cell; relax when the new pair is strictly better.
S"
Rahul J. - "Constraints: 4-direction moves; no mode switching (pick exactly one of {1=bicycle, 2=bike, 3=car, 4=bus} for the full trip).
Per-mode search:
If a mode’s per-step time/cost are uniform, run BFS on allowed cells. Then totaltime = steps × timeperstep, tie-break by steps × costper_step.
If time/cost vary by cell (given matrices), run Dijkstra per mode minimizing (totaltime, totalcost) lexicographically. Maintain the best ⟨time, cost⟩ per cell; relax when the new pair is strictly better.
S"See full answer
"Questions:
Is it Top 10 most frequently listened songs in last 7 days for user?
If not, is it for a geographical location, like top 10 most listened songs in last 7 days in US/India/UK?
Assumption:
Lets assume that we are looking for songs which are top 10 most frequently listened songs for a user in his last 7 days.
First, the system should have a counter to keep track of how many times a audio file is played for a user profile.
Second, the system should also have date variable to store"
Dhiraj K. - "Questions:
Is it Top 10 most frequently listened songs in last 7 days for user?
If not, is it for a geographical location, like top 10 most listened songs in last 7 days in US/India/UK?
Assumption:
Lets assume that we are looking for songs which are top 10 most frequently listened songs for a user in his last 7 days.
First, the system should have a counter to keep track of how many times a audio file is played for a user profile.
Second, the system should also have date variable to store"See full answer
"CQs:
Help? → any sort of help
Customer support?
On App guidance?
Help for creators?developers? → no
Currently it is FAQ based, predefined app guide for the new users, customer support for critical cases
Any specific problem came out in research or want me to brainstorm → brainstorm
Improve what? →
CSAT - lagging metric, should not be only goal
Time spent in on app help → key goal is reduce this
No constraints
Geo - US
Users
E"
Sumit P. - "CQs:
Help? → any sort of help
Customer support?
On App guidance?
Help for creators?developers? → no
Currently it is FAQ based, predefined app guide for the new users, customer support for critical cases
Any specific problem came out in research or want me to brainstorm → brainstorm
Improve what? →
CSAT - lagging metric, should not be only goal
Time spent in on app help → key goal is reduce this
No constraints
Geo - US
Users
E"See full answer
"CQs:
Swiggy? → instamart or food delivery → consider both
Why do we want to increase AOV right now? Is it not at the desired level or exploration? → let’s say exploration
Swiggy is a public company → goal is profit
Biggest bite in profit is delivery cost hence delivery cost/unit revenue should be minimised
Delivery cost = (fixed base cost + distance * x) * probability of spoil cases
Can be done by
Lowering delivery cost → seems challenging
"
Sumit P. - "CQs:
Swiggy? → instamart or food delivery → consider both
Why do we want to increase AOV right now? Is it not at the desired level or exploration? → let’s say exploration
Swiggy is a public company → goal is profit
Biggest bite in profit is delivery cost hence delivery cost/unit revenue should be minimised
Delivery cost = (fixed base cost + distance * x) * probability of spoil cases
Can be done by
Lowering delivery cost → seems challenging
"See full answer