Intermediate interview questions in AI & ML, page 5
U-Net architecture and its skip connections
U-Net has a contracting encoder, an expanding decoder, and skip connections that concatenate matching-resolution encoder features into the decoder to recover spatial detail lost in downsampling.
What events trigger automatic model retraining beyond code changes?
This tests whether you treat ML pipelines as event-driven systems, not just software CI/CD. A strong answer lists data drift, scheduled cron jobs, production metric degradation, schema changes, and upstream data pipeline completion.
When is streaming better than batch, and what are its infrastructure challenges?
Tests if you separate low-latency decisions from analytics and know ops pain. Good answer: fraud detection vs batch; cite exactly-once, state recovery, backpressure, schema drift. Red flag: calling streaming faster batch while ignoring backpressure or state.
How does Mask R-CNN do instance segmentation?
Faster R-CNN backbone plus RPN, then RoIAlign and a parallel mask head predicting per-class binary masks.
How would you implement shadow deployment and which metrics justify promotion?
Tests zero-impact validation when feedback loops are broken. Mirror traffic to a shadow variant, log predictions, and compare latency, errors, and drift against SLAs. Red flag: calling it A/B testing or claiming live business metrics from unserved responses.
What is GAN mode collapse, its causes, and two mitigations?
Define mode collapse as diversity loss to few modes; cite discriminator imbalance and lenient JS loss; give two fixes: WGAN and mini-batch discrimination.
How to improve coarse segmentation boundaries?
Skip connections and higher-resolution features, boundary-aware losses, and point-based or CRF refinement.
What infrastructure is needed for a Continuous Training pipeline?
Tests event-driven ML system design beyond CI/CD. Strong answers name orchestrators, feature stores, model registries, and validation gates, mapping triggers to retraining and promotion. Red flag: conflating CT with CI/CD or skipping model validation.
How does text guide Stable Diffusion via U-Net cross-attention?
Tests whether you know text embeddings condition the U-Net through cross-attention. Good answers explain that image features query text keys and values at every layer. Red flag: claiming the prompt is concatenated to the image latent.
Evaluating image generation: FID and IS
FID compares feature distributions of real and generated images, lower is better; Inception Score rewards confident, diverse classes but ignores real data.
How do you detect and handle outliers in multivariate datasets?
This tests statistical judgment beyond univariate thresholds. A strong answer names multivariate methods like Mahalanobis distance or isolation forests, PCA or pair plots for visualization, and checks for measurement error before removal.
Brightness constancy and small-motion assumptions
Brightness constancy says a point's intensity is invariant under motion; small motion lets you linearize via Taylor expansion.
Where to place feature transformations: client, serving API, or upstream service?
Tests separation of concerns in ML systems. Client causes duplication and skew; serving API couples compute to requests; dedicated service adds a network hop but centralizes logic. Red flag: ignoring training-serving skew.
How do you visualize clusters in 100-dimensional numerical data?
Tests PCA versus t-SNE tradeoffs for high-dimensional visualization. Strong answers use PCA first for global variance and outliers, then t-SNE with perplexities 5-50 run to stability. Red flag: interpreting t-SNE cluster sizes or distances as literal metrics.
Kalman filter for bounding-box tracking
State, transition, measurement models, and process plus measurement noise; predict then correct each frame. State holds box position and velocity; measurement is the detected box.
Design a system for batch scoring millions of customer records daily
Shard jobs via scheduler; partition storage; right-size CPU/GPU on spot; retry.
How does a U-shaped EDA pattern influence feature engineering?
Add age squared or splines; trees handle splits but explicit terms aid linear models.
Designing a Visual Question Answering system
Encode the image with a vision backbone, encode the question with a text encoder, fuse them via cross-attention into a joint representation, then decode or classify the answer.
3D CNNs vs two-stream action recognition
3D CNNs learn spatiotemporal filters end to end but are heavy; two-stream splits RGB appearance and precomputed optical flow, strong but costly to compute flow.
How Stable Diffusion generates images
The text encoder turns the prompt into embeddings, the U-Net predicts noise to remove conditioned on those embeddings, and the scheduler controls how noise is stepped down over iterations in…
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