Interview questions in AI & ML, page 24
Fault-tolerant checkpointing for thousand-GPU pre-training
Checkpoint weights, optimizer state, RNG, and data position together; use asynchronous sharded writes and automated detect-restart-resume.
pandas .apply() versus vectorized operations
Apply runs a Python function per row or column, flexible but slow due to per-element looping; prefer vectorized ops; use apply only for custom logic with no vectorized equivalent.
Point cloud vs voxel grid vs NeRF
Point clouds are sparse and fast but unstructured, voxels are regular for collision checks but memory-heavy, NeRFs render photorealistically but are slow.
Why systematically track ML experiments and what should you log?
This tests reproducibility mindset over bookkeeping. A strong answer names three motivations—reproducibility, selection, debugging—and three logs: hyperparameters, metrics, and code versions.
Prompt engineering to curb extraction hallucinations
Ground strictly in source, allow null for missing fields, enforce a schema, and use few-shot examples; acknowledge prompting cannot fully eliminate it.
When should you keep outliers, not drop them?
Keep them in fraud or anomaly detection, use robust models and metrics.
Zero-shot classification with CLIP
Encode image and label prompts into a shared space, compare via cosine similarity, pick the highest.
Describe a Model Registry and how it differs from versioned storage
It tests governance and lifecycle metadata beyond file storage. A strong answer covers lineage, stage transitions, approval gates, and artifact metadata, contrasting with buckets that only store file versions.
Self-consistency over chain-of-thought
Sample multiple CoT paths at nonzero temperature and majority-vote the final answer; cost scales with the number of samples.
Explain KNN or MICE imputation principles
KNN borrows from similar rows, MICE models each variable from the others iteratively and creates multiple datasets.
Contrastive learning vs masked image modeling
Contrastive aligns augmented views via instance discrimination; MAE reconstructs masked patches; they differ in augmentation and fine-tuning.
Reproduce a six-month-old model using experiment tracking
Trace code commit, dataset version, feature pipeline, hyperparameters, dependency manifest, and random seeds through a model registry.
Why chain-of-thought helps large models but not small ones
Small models lack reliable multi-step reasoning, so CoT just adds error-prone steps; adapt by using few-shot/fine-tuning or distillation for small tiers.
Design a SQL upsert from a staging table
Define a stable key, use MERGE or INSERT ON CONFLICT, dedupe the staging set first, run in a transaction.
When a homography is a valid model
Homography holds for pure rotation or a planar scene; it fails with translation plus 3D parallax, where epipolar geometry applies.
Debug sudden model degradation using experiment tracking and model registry
Tests unified use of experiment tracking and registry lineage. Great answers verify the exact production artifact, inspect linked training data and hyperparameters, compare input distributions, and check dependency metadata.
Handling a 401 error in an LLM agent's tool call
Catch the tool error, return a structured observation to the LLM, and distinguish recoverable retries from terminal failures needing re-plan or escalation.
Handle source schema changes without downtime
Add columns as nullable additive changes, version the schema, use formats like Iceberg or Parquet that support evolution, backfill new types safely.
Challenges deploying a model on edge hardware
Limited memory and compute cause latency, thermal and power limits, accuracy loss from compression, operator support gaps.
Design a centralized model registry for a large enterprise
Tests ML artifact governance at scale. Strong answers cover immutable versioned artifacts with dependency manifests, a framework-agnostic API, and pluggable deployment targets. Red flag: treating models as opaque files without environment reproducibility.
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