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Programmatically create an ad via a marketing API
Authenticate, create a campaign with an objective, an ad set with budget and targeting, a creative with the copy, then the ad linking them.
Design a programmatic SEO landing-page system
Structured data plus templates, genuinely unique per-page data, static build-time generation, quality gates and indexation control.
Explain TF-IDF and its use in SEO analysis
Term frequency times inverse document frequency surfaces distinctive terms; comparing a draft to top-ranking pages reveals missing topical terms.
Data model for an editorial calendar
A content entity with publish_date and a status enum, a foreign key to authors, indexes on date and status.
Design a next-best-article recommender
Content-based on article embeddings, collaborative on behavior, a hybrid blend, popularity or onboarding fallbacks for new users.
Structuring release notes for breaking API changes
Lead with a summary and why, group breaking changes with before/after migration steps, give deprecation timelines and an upgrade path.
TF-IDF Analysis
TF-IDF weights a term by how often it appears in a document against how rare it is across the corpus, surfacing words distinctive to that document rather than ones common everywhere, which powers keyword extraction and search ranking.
Design an active learning loop for detection
Seed-train, score the pool by uncertainty plus diversity, batch to annotators, retrain, repeat.
What does N-way K-shot classification mean?
N is classes per episode, K is labeled examples per class in the support set, prediction is on a separate query set.
Filter-based vs optimization-based SLAM
EKF folds past poses into one Gaussian; optimization keeps a sparse graph and re-linearizes; the latter wins on accuracy and loop closure.
Design real-time multi-object tracking for AV
Fast detector, Kalman motion model, Hungarian association on IoU plus appearance, track lifecycle for occlusions.
Loss functions for imbalanced medical segmentation
Cross-entropy is swamped by background; Dice optimizes overlap directly; Focal down-weights easy pixels.
How is IoU computed and why prefer mIoU?
IoU is intersection over union of predicted and true pixels; mIoU averages per class; pixel accuracy is dominated by background.
Homography vs fundamental matrix degeneracy
Homography fits planar scenes or pure rotation; fitting a fundamental matrix there is degenerate because points lack depth variation.
PTQ versus QAT for model quantization
PTQ quantizes a trained model with calibration data, fast but may lose accuracy; QAT simulates quantization in training so weights adapt.
Why averaging shots reduces image noise
Random noise averages out while signal stays, so SNR rises with the square root of frame count; limits include motion and fixed-pattern noise.
The role of anchor boxes in detectors
Anchors are reference boxes of set scales and ratios; the model regresses offsets, framing detection as classification plus refinement; downsides are tuning, imbalance.
Triangulating 3D points from two views
Back-project each 2D point into a ray, intersect rays from both cameras using known poses, solve via DLT and refine by minimizing reprojection error.
Single vs multi-image super-resolution
SISR hallucinates detail from one image, multi-image fuses sub-pixel shifts; pixel losses blur, perceptual or GAN losses add realism.
Non-maximum suppression in detection
Detectors emit many overlapping boxes per object; NMS keeps the highest-scoring box and removes others above an IoU threshold.