The graph flattening part tripped me up more than I expected.
Clarify the graph representation and data type, then systematically compute memory by flattening all dimensions and multiplying by the bit size of the data type. Convert bits to bytes and consider any overheads or alignment requirements.
Pro tip: Always state your assumptions about the graph structure and data type, and mention that in real systems, memory alignment and padding can increase the actual footprint beyond the theoretical minimum.
Identify how the DDN model is represented as a graph: number of nodes, edges, and any feature dimensions per node/edge. Confirm if it's stored as an adjacency matrix, edge list, or other format.
Ask or assume the data type used for storing graph elements (e.g., float32, int64) and note its bit size (e.g., 32 bits for float32).
Multiply the sizes of all dimensions to get the total number of elements. For example, if the graph has N nodes with D-dimensional features, total elements = N * D.
Multiply total elements by the bit size of the data type to get total bits, then divide by 8 to convert to bytes (and optionally to KB/MB).
Mention that actual memory may be larger due to metadata, pointers, or padding for alignment, and discuss trade-offs if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Walk through a real coding problem you solved using AI, narrating the exact prompt you wrote, the code the AI generated, and the specific bugs you found and fixed. Emphasize how you validated the AI output rather than trusting it blindly, and tie your debugging process to Amazon's Leadership Principles like Customer Obsession and Ownership.
Pro tip: Choose a problem where the AI's first output was subtly wrong—not obviously broken—so you can showcase your debugging depth and judgment. Explicitly state what you would have done differently or how you'd prompt better next time, showing iterative learning.
Briefly describe the coding problem, its constraints, and why you chose to use an AI tool. Keep it concise so you leave time for the prompt, code, and debugging.
Recite the user query you wrote to the AI, highlighting how you specified inputs, outputs, edge cases, and language/framework. Explain why you phrased it that way.
Summarize the code the AI produced, noting any assumptions it made or parts that looked suspicious. Avoid reading every line; focus on the critical sections.
Walk through the debugging process: how you identified the bug (e.g., test failure, edge case, logic error), the root cause, and the fix you applied. Mention any tests you wrote to verify.
Summarize lessons learned about prompting and debugging AI code, and how you'd apply this to future tasks. Connect to broader engineering principles like code review and testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.