Computer vision interviews test far more than CNN theory. Expect questions on detection architectures, segmentation trade-offs, evaluation metrics like mAP and IoU, and the production realities of deploying vision models — latency budgets, edge constraints, and domain shift. This guide covers the full scope with answer frameworks and sample responses for the questions that actually determine hiring decisions.
Start Free Practice Interview →Computer vision engineering sits at the intersection of deep learning and real-world perception. Unlike a general deep learning engineer who might work across any neural network application, a CV engineer specializes in making machines see — and the interview questions reflect that specialization.
Expect to explain why your detector uses FPN instead of a single-scale feature map, how you'd reduce false positives in a manufacturing inspection line without hurting recall, and what happens to your mAP when you change the IoU threshold from 0.5 to 0.75. Interviewers also care about the full pipeline: data collection and labeling strategy, augmentation, training, post-processing (NMS and its variants), and deployment under latency and memory constraints.
This guide is organized by interview topic area: detection and segmentation architectures first, then metrics and evaluation, training and data strategy, production deployment, and domain-specific variations that change what interviewers prioritize.
The computer vision engineer role has broadened significantly. Early CV engineers were primarily researchers implementing papers. Today, the role is deeply production-oriented, with responsibilities spanning the entire pipeline from data to deployment.
Detection and segmentation system design — selecting and adapting architectures for specific visual tasks. This means understanding not just which model to use but why: anchor-based vs anchor-free, one-stage vs two-stage, and how the choice affects speed, accuracy, and deployment complexity.
Data pipeline and labeling strategy — designing annotation workflows, managing labeling quality, handling class imbalance, and building augmentation pipelines. In production CV, data quality often matters more than model architecture.
Evaluation and error analysis — going beyond a single mAP number to understand where and why a model fails. This includes per-class analysis, failure mode categorization, and connecting model errors to business impact.
Production optimization — deploying vision models under real-world constraints: latency requirements, memory limits, and throughput demands. This involves quantization, pruning, distillation, and serving infrastructure design.
Domain adaptation and robustness — handling the gap between training data and production conditions. Different cameras, lighting changes, weather, motion blur, and distribution shift over time. This is often the hardest part of production CV.
Computer vision engineer overlaps heavily with deep learning engineer and ML engineer, and many companies use the titles loosely. The comparison below clarifies where the interview focus differs — this helps you prioritize what to prepare.
| Dimension | Computer Vision Engineer | Deep Learning Engineer | ML Engineer |
|---|---|---|---|
| Core focus | Visual perception — detection, segmentation, tracking, and vision-specific pipelines | Neural network architecture design, training optimization, and model internals across any domain | End-to-end ML pipelines — data processing, model selection (classical + deep), feature engineering, serving |
| Typical interview questions | Compare Faster R-CNN vs YOLO vs FCOS, explain NMS, calculate mAP at different IoU thresholds | Derive backprop, explain why LayerNorm beats BatchNorm in transformers, design a training pipeline | Design a feature store, compare XGBoost vs neural net for tabular data, build an inference pipeline |
| Math expectations | Moderate — projective geometry, convolution math, IoU computation, precision-recall curves | High — linear algebra, calculus, probability, information theory on whiteboard | Moderate — statistics, probability, some linear algebra |
| Data focus | Image/video annotation quality, augmentation strategy, domain shift between cameras and environments | Dataset construction for training, preprocessing, data loading efficiency | Feature engineering, data drift, pipeline reliability, data quality at scale |
| Production concerns | Inference latency for video, NMS overhead, camera calibration, edge deployment (TensorRT, ONNX) | Training efficiency, GPU utilization, quantization for general deployment | Pipeline reliability, A/B testing, feature freshness, serving infrastructure |
| Domain knowledge | High — camera optics, sensor characteristics, lighting, specific verticals (AV, medical, manufacturing) | Low to moderate — domain-agnostic architecture expertise | Low to moderate — domain varies by company |
Detection and segmentation are the bread and butter of CV interviews. Interviewers expect you to know the major architecture families, understand why each design decision was made, and reason about trade-offs in the context of a specific deployment scenario.
This is the most fundamental architectural question in object detection. Your answer reveals whether you understand the design trade-offs or just know model names.
Two-stage (Faster R-CNN family) generate region proposals first, then classify and refine — higher accuracy, especially for small objects and crowded scenes, but slower. One-stage (YOLO, SSD, RetinaNet, FCOS) predict directly from feature maps — faster, but historically less accurate on hard cases. Key innovations: focal loss solved class imbalance for one-stage. Anchor-free approaches eliminated hand-designed anchors. Choose two-stage when accuracy on hard cases matters more than speed. Choose one-stage when latency matters.
Two-stage detectors like Faster R-CNN use a region proposal network to generate candidate boxes, then a second stage classifies each proposal and refines the bounding box. This two-pass approach gives the model two chances to get it right, which helps with small objects and cluttered scenes. The cost is speed — the second stage runs per-proposal. One-stage detectors predict class and location directly from feature maps. The historical weakness was accuracy: the massive imbalance between background and foreground locations overwhelmed training. RetinaNet's focal loss fixed this by downweighting easy negatives. Today there's also the anchor-based vs anchor-free split. Anchor-free detectors (FCOS, CenterNet) predict center points and distances to box edges, eliminating hyperparameter tuning for anchor ratios and scales. In practice, I'd choose a one-stage anchor-free detector as the default for most production systems because of speed and simpler configuration. I'd switch to two-stage for datasets with dense small objects, heavy occlusion, or where accuracy on hard cases is the primary metric.
Multi-scale feature handling is critical for detecting objects at different sizes. FPN is the standard approach and understanding it reveals architectural depth.
The problem: objects appear at vastly different scales. Early CNN layers have high resolution but weak semantics; deep layers have strong semantics but low resolution. FPN adds a top-down pathway with lateral connections that merge high-resolution low-level features with low-resolution high-level features at each scale. The result is feature maps at multiple resolutions, all with strong semantic information. FPN is standard in nearly all modern detectors and also used in instance segmentation (Mask R-CNN).
NMS is a critical post-processing step that every detection pipeline uses. Its failure modes cause real production issues.
NMS removes duplicates: sort by confidence, keep the top box, suppress all boxes with IoU above threshold, repeat. Failure modes: (1) crowded scenes — suppresses valid overlapping detections; (2) hard IoU threshold cutoff; (3) sequential nature makes it a latency bottleneck on GPU. Alternatives: Soft-NMS (decays confidence instead of hard suppression), Weighted Box Fusion (merges via weighted averages), and DETR (removes NMS entirely with set prediction and Hungarian matching).
NMS works by sorting detections by confidence, taking the highest-scoring box, removing all other boxes that overlap it above an IoU threshold, then repeating. It has three failure modes that matter in production. First, crowded scenes: if two people stand close together and their boxes exceed the IoU threshold, NMS suppresses one. Soft-NMS addresses this by decaying confidence of overlapping boxes instead of removing them. Second, the IoU threshold is a hard binary decision — no threshold is right for all scenes. Third, NMS is sequential and can't be parallelized efficiently on GPU, making it a latency bottleneck in real-time systems. Weighted Box Fusion merges overlapping boxes into a single weighted-average box for better localization. DETR sidesteps NMS entirely by framing detection as set prediction with Hungarian matching — no duplicate removal needed.
Segmentation has multiple formulations with different architectures. Confusing them is a red flag.
Semantic: classify every pixel into a category (no instance distinction). Architectures: FCN, U-Net, DeepLab. Instance: detect individual objects and produce a mask for each. Architectures: Mask R-CNN, YOLACT, SOLOv2. Panoptic: combines both — segments 'stuff' semantically and 'things' by instance. Architectures: Panoptic FPN, MaskFormer, Mask2Former (unified transformer-based approach handling all three).
YOLO is the most commonly deployed detection family. Understanding its evolution shows whether you follow the field.
YOLOv1: single-pass grid-based, fast but weak on small objects. v2/v3: anchor boxes, multi-scale prediction, batch norm — improved small object detection. v4/v5: bag-of-freebies (mosaic augmentation, CutMix) and bag-of-specials (CSP backbone, PANet neck). Modern variants (v8, RT-DETR): anchor-free heads, decoupled classification/regression, transformer necks. Key insight: most improvements came from training recipe changes rather than fundamental architecture changes.
Tracking is essential for video-based CV. Many CV roles involve video processing.
Tracking-by-detection: run detector per frame, associate detections across frames. SORT: Kalman filter for motion prediction, Hungarian algorithm for IoU-based assignment. Fast but fails with occlusion. DeepSORT: adds Re-ID appearance embedding for visual similarity when IoU matching fails. More robust but slower. ByteTrack: uses low-confidence detections too — matches high-confidence first, then remaining tracks with low-confidence detections, recovering partially occluded objects. Production challenges: ID switches, Re-ID cost at high object counts, ego-motion compensation.
Tests whether you can apply architectural knowledge to a real scenario with specific constraints.
Small object detection considerations: (1) high-resolution input — don't downsample aggressively, or use tiling/sliding window; (2) FPN with strong low-level feature paths; (3) anchor sizes tuned to defect size distribution; (4) augmentation that preserves small object visibility; (5) strict IoU thresholds since localization matters; (6) extreme class imbalance — focal loss or hard example mining; (7) consider two-stage approach with high-resolution second stage.
Metrics questions separate candidates who genuinely evaluate their models from those who just report a single number. Interviewers want to see that you understand what metrics actually measure, when they mislead, and how to connect model performance to business outcomes.
mAP is the standard detection metric, but many candidates can't explain what it actually computes. This is a litmus test for CV competency.
AP for a single class: sort detections by confidence, compute precision and recall at each threshold, compute area under the PR curve. mAP averages across all classes. AP50 uses IoU ≥ 0.5 (lenient), AP75 uses IoU ≥ 0.75 (strict localization). COCO mAP averages across IoU thresholds from 0.5 to 0.95. Also mention AP_small, AP_medium, AP_large — these reveal size-specific weaknesses.
mAP starts with per-class Average Precision. For each class, rank all detections by confidence, then walk down the list computing precision and recall. A detection is a true positive if it has IoU above the threshold with an unmatched ground truth box. The PR curve is summarized as area under the curve — that's the AP for one class. mAP is the mean across all classes. The IoU threshold matters enormously. AP50 only requires 50% overlap — most modern detectors score well here. AP75 requires 75% overlap, testing localization precision. A model might score 60 AP50 but only 35 AP75, meaning it finds objects but doesn't box them tightly. COCO mAP averages across ten IoU thresholds from 0.5 to 0.95. What I find most useful in practice is the size-based breakdown: AP_small, AP_medium, AP_large. This almost always reveals weakness on small objects. If I'm presenting results, I always show the size breakdown rather than just the headline mAP.
Segmentation metrics are less standardized than detection metrics. Choosing the wrong one can mask serious failures.
IoU per class, then mIoU — standard for semantic segmentation. Dice coefficient — common in medical imaging. Pixel accuracy — misleading with class imbalance. Boundary F1 — evaluates edge quality. Panoptic Quality (PQ) for panoptic segmentation. What they miss: pixel metrics don't capture topology, and they weight all pixels equally — a mistake on a critical boundary may matter more than one in a region center.
Aggregate metrics hide per-class failures. This tests real error analysis methodology.
Diagnosis: (1) per-class AP, (2) confusion matrix — confused with a specific class? (3) false negatives — small, occluded, unusual poses? (4) training data — underrepresented or noisy annotations? (5) visualize predictions. Fixes depend on cause: class imbalance → oversampling/focal loss; annotation quality → re-label; hard examples → targeted augmentation or hard example mining; confusion with similar class → more discriminative features.
Confidence calibration is critical for production decisions but often overlooked.
Calibration means predicted confidence matches actual accuracy — 90% confident should be correct ~90% of the time. Most neural networks are overconfident. Production impact: confidence thresholds drive decisions (flag for human review, auto-approve). Measuring: Expected Calibration Error (ECE), reliability diagrams. Fixing: temperature scaling (simple, effective), Platt scaling, histogram binning. Temperature scaling is the standard first approach.
Understanding business context of metrics shows you can connect performance to real-world impact.
Precision matters when false positives are costly: manufacturing defect detection (stopping production line), content moderation (blocking legitimate content), AV non-critical alerts (eroding trust). Recall matters when false negatives are costly: medical screening (missing a tumor), security surveillance (missing a threat), AV pedestrian detection (safety failure). Many systems need different operating points for different contexts.
In production computer vision, data strategy often determines model quality more than architecture choice. These questions test whether you understand the full data pipeline — from collection and annotation through augmentation and training.
Augmentation is critical for CV generalization, but naive application can introduce problems.
Safe and effective: horizontal flip, random crop (with box adjustment), color jitter, mosaic augmentation, CutMix/MixUp. Potentially harmful: aggressive rotation (if objects have canonical orientation), extreme aspect ratio changes, augmentations that cut objects without adjusting labels, heavy blur (destroys small object features). Key principle: any spatial augmentation must also transform bounding box annotations. Any augmentation that removes an object must remove its annotation.
Class imbalance is the norm in CV, not the exception.
Data level: oversample rare classes, copy-paste augmentation, targeted data collection. Loss level: focal loss, class-weighted CE, OHEM. Architecture level: balanced feature sampling across FPN levels. Evaluation: report per-class AP, not just mAP. Practical note: foreground-background imbalance is often a bigger problem than inter-class imbalance, and focal loss addresses both.
Domain shift is the #1 cause of CV model failures in production.
Common sources: different cameras, lighting conditions, environment changes, concept drift. Detection: monitor confidence distributions, track per-class performance on periodic audits, compare feature distributions. Mitigation: domain randomization during training, style transfer for synthetic-to-real gaps, test-time augmentation, periodic retraining with production data, and maintaining diverse training sets covering deployment conditions.
Data leakage in CV is subtler than in tabular ML.
Common leakage: (1) near-duplicate video frames split across train/val — fix: split by video/sequence; (2) same object in multiple images in both splits — fix: split by object ID; (3) metadata leakage (EXIF, filenames correlating with labels) — fix: strip metadata; (4) augmentation before splitting — fix: always split first; (5) temporal leakage in sequential tasks. Prevention: define split strategy before looking at data, validate with independently collected test set, run leakage audit with trivially simple models.
Labeling strategy is a core CV engineering responsibility.
Start with clear guidelines (visual examples of edge cases). Pilot round to test guidelines and measure inter-annotator agreement. QA: multi-annotator overlap on subset, consensus resolution, automated checks. Active learning: label small set, train initial model, use uncertainty to select next labeling batch. Key metric: inter-annotator agreement — if two annotators can't agree, neither can your model.
Production CV is where many candidates fall short. Training a model that works on a benchmark is one thing — deploying it to run at 30fps on an edge device while handling real-world variation is another.
Edge deployment is one of the most common CV production requirements.
Profile where latency lives (backbone, neck, head, NMS). Optimization layers: (1) lightweight backbone (MobileNet, EfficientNet) or purpose-built edge detector (YOLOv8-nano); (2) INT8 quantization via TensorRT — 2-3x speedup with minimal loss; (3) export format (TensorRT for NVIDIA Jetson, TFLite for mobile); (4) input resolution reduction — computation is quadratic in resolution; (5) NMS optimization — limit max detections, batched NMS. Measure end-to-end including preprocessing and postprocessing.
First I'd profile the full pipeline, not just the model forward pass. On edge devices, preprocessing — resizing and normalizing on CPU — can take longer than model inference on GPU. For the model: architecture selection is key. A purpose-built small model usually outperforms a compressed large model at the same latency. Next, INT8 quantization via TensorRT typically gives 2-3x speedup with less than 1 mAP drop. I'd calibrate using representative data from the production environment. Input resolution is the biggest single lever — going from 640 to 416 reduces computation by roughly 2.4x. For NMS: cap maximum detections, increase confidence threshold to filter early, and use TensorRT's built-in NMS plugin. Finally, if processing a video stream, batch multiple frames for better GPU utilization at the cost of slightly higher per-frame latency.
Video processing has different constraints than single-image inference. This tests systems thinking.
Bottlenecks by stage: (1) video decoding — use hardware-accelerated NVDEC; (2) preprocessing — resize/normalize on GPU not CPU; (3) model inference — batch frames, use TensorRT; (4) postprocessing — NMS, tracking, business logic; (5) I/O — writing results, downstream services. Minimize CPU-GPU memory transfers by keeping the full pipeline on GPU. For tracking, association cost scales with number of tracked objects.
Camera calibration is a production reality invisible in benchmark datasets.
Calibration covers intrinsic parameters (focal length, principal point, distortion) and extrinsic (position, orientation). Impact: lens distortion curves straight lines affecting detection; different focal lengths change apparent object size; 3D reasoning requires accurate calibration. Approach: undistort images before the model (OpenCV), include calibration metadata in pipeline, recalibrate when cameras change, train with augmentations simulating calibration variation.
Choosing the right inference runtime directly affects latency and deployment flexibility.
TensorRT: NVIDIA's optimizer — best raw performance on NVIDIA GPUs (data center and Jetson). Vendor-locked but gives biggest speedup via kernel fusion and INT8 calibration. ONNX Runtime: cross-platform, good default for portability or multi-hardware. Performance is good but usually doesn't match TensorRT on NVIDIA. TFLite: optimized for mobile (Android/iOS) and microcontrollers, best for on-device inference. Typical pipeline: develop in PyTorch → export to ONNX → compile to TensorRT for NVIDIA targets or keep ONNX Runtime for cloud, convert to TFLite for mobile.
Computer vision interviews shift significantly depending on the industry vertical. While the fundamentals are shared, the specific questions, constraints, and domain knowledge vary enough that you should tailor your preparation.
AV interviews focus on multi-sensor perception: camera, LiDAR, and radar fusion. Expect questions on 3D object detection (PointPillars, CenterPoint), bird's-eye view representations, and multi-object tracking with motion prediction. Real-time constraints are strict — perception must run at sensor frame rate with bounded worst-case latency. Safety is paramount: failure mode analysis, sensor redundancy, edge cases like unusual road users or adverse weather. ODD (Operational Design Domain) and functional safety concepts may come up.
Medical CV interviews emphasize sensitivity and specificity, calibration, and regulatory awareness. Metrics shift from mAP to Dice coefficient, sensitivity/specificity, and AUC-ROC. Dataset bias is a major concern — models trained on one hospital's data may fail at another. Expect questions on small datasets (transfer learning, self-supervised pretraining), 3D volumetric processing (CT, MRI), and clinical validation. Regulatory frameworks (FDA clearance for SaMD) are relevant context.
These interviews focus on anomaly detection (detecting defects never seen before), extreme class imbalance (99.9%+ good parts), and false positive cost (stopping a production line for a false alarm is expensive). Expect questions on one-class classification, few-shot learning for new defect types, and handling camera or lighting changes in deployment. Throughput matters — inspection systems may process hundreds of parts per minute.
Expect questions on fine-grained visual recognition (distinguishing similar products), image search and retrieval (embedding space design, similarity metrics), and OCR/document understanding. Scale matters — catalogs may have millions of products, and the system needs to handle user-uploaded images of varying quality.
CV coding interviews are different from standard software engineering interviews. Instead of LeetCode-style problems, expect implementation questions that test whether you can translate CV concepts into working code. These typically involve NumPy or PyTorch.
IoU is the most fundamental computation in object detection. If you can't implement it from scratch, interviewers question whether you understand the metrics you report.
Boxes as (x1, y1, x2, y2). Intersection: max of x1s, max of y1s, min of x2s, min of y2s. Clamp to zero if no overlap. Intersection area = max(0, x_right - x_left) × max(0, y_bottom - y_top). Union = area_box1 + area_box2 - intersection. IoU = intersection / union. Handle zero union. For batch computation, vectorize with NumPy broadcasting.
NMS is in every detection pipeline. Implementing it tests whether you understand the algorithm you describe conceptually.
Input: boxes (N×4), scores (N), IoU threshold. Sort indices by score descending. While indices remain: take top-scoring index, add to keep list, compute IoU between that box and all remaining, remove indices where IoU exceeds threshold. Return kept indices. Vectorize the inner IoU computation. For Soft-NMS, decay scores instead of removing.
Tests whether you understand the critical constraint: spatial transforms must be applied to both the image and annotations.
Implement: horizontal flip (flip image, transform x-coordinates: new_x1 = width - old_x2), random crop (adjust box coordinates relative to crop origin, remove boxes that fall outside), resize (scale coordinates proportionally). Validate resulting boxes are still valid (positive area, within bounds). Libraries like Albumentations handle this, but understand the mechanics.
mIoU computation tests per-class evaluation understanding and efficient tensor operations.
Input: predicted mask (H×W), ground truth mask (H×W), number of classes. Per class: intersection (pixels where both pred and gt equal that class), union (pixels where either equals that class), IoU = intersection / union (exclude zero-union classes). Vectorize: build confusion matrix (N×N), derive IoU per class from diagonal and row/column sums.
CV roles require collaboration with data labeling teams, product managers, and hardware/infrastructure engineers. Behavioral questions test whether you can navigate these cross-functional relationships and make pragmatic decisions.
The train-production gap is the defining challenge of applied CV.
STAR format. Describe the gap (what metric dropped). Root cause analysis — domain shift, data quality, or pipeline bug? Walk through the fix and what you changed in your process to prevent recurrence. Strongest answers show you changed your evaluation methodology, not just the model.
Data collection can be an infinite time sink. This tests pragmatic judgment.
Minimum viable dataset: enough examples per class to learn basic visual features. Check inter-annotator agreement — fix guidelines before collecting more. Train a baseline early — its error analysis tells you what's missing. Iterate: collect more data targeted at failure modes rather than uniformly expanding. Goal is a data flywheel where each training round informs the next collection round.
CV engineers frequently face unrealistic accuracy or latency requirements.
Choose an example about a real constraint (e.g., PM wanted 99% accuracy but labeling noise ceiling was 95%, or PM wanted real-time but target device lacked GPU). Show how you quantified the trade-off, presented data, and found a compromise — tiered system, different operating point, or phased approach.
Reading frameworks is a start — but CV interviews reward the ability to reason through architecture trade-offs and deployment constraints under pressure. Our AI simulator generates role-specific questions, times your responses, and scores both technical depth and communication clarity.
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If you only have limited prep time, focus on object detection architectures and evaluation metrics. Be able to explain the difference between one-stage and two-stage detectors, walk through how mAP is computed, and describe your approach to error analysis when a model underperforms. These topics come up in nearly every CV interview regardless of the specific domain. After that, prioritize whatever matches the company's domain — if they do autonomous driving, study 3D detection and sensor fusion; if they do medical imaging, study segmentation metrics and calibration.
It depends on the role. Most modern CV engineer positions are deep-learning-first, so you'll spend most interview time on neural architectures, training, and deployment. However, classical CV concepts still appear in production: camera calibration uses traditional geometric methods, image preprocessing is still relevant, and some edge deployment scenarios use classical features because they're faster. Companies working with 3D vision, robotics, or augmented reality are more likely to test classical CV. If the job description mentions OpenCV, stereo vision, or SLAM, prepare for it. Otherwise, focus on deep learning approaches.
For senior roles, very important. You should be able to discuss recent papers — not recite every detail, but explain the key idea, why it matters, and its limitations. For mid-level roles, know the foundational papers in detection (R-CNN family, YOLO, DETR), segmentation (U-Net, DeepLab, Mask R-CNN), and domain-specific papers relevant to the company. The most common mistake is knowing what a model does but not why it was designed that way.
Yes, but they're usually different from standard software engineering coding interviews. Expect implementation questions like computing IoU between bounding boxes, implementing NMS, or writing a data augmentation pipeline. Some companies also include standard algorithm questions, especially at larger tech companies. At CV-focused companies and startups, coding tends to be more domain-specific. Be comfortable with PyTorch and NumPy — you may need to implement a custom loss function, write a training loop, or manipulate tensors.
CV engineers typically progress from implementing existing architectures to designing systems end-to-end and leading technical strategy. The senior path branches into technical lead (owning the vision system for a product), research engineering (bridging research and production), or management (leading a CV team). Domain expertise becomes increasingly valuable — a CV engineer with deep autonomous driving or medical imaging experience is more specialized and harder to replace than a generalist. Some CV engineers transition to broader ML or AI engineering roles, and the skills transfer well.
CV interviews are domain-specialized. A deep learning engineer interview might ask you to derive backpropagation or explain transformers in the abstract. A CV interview asks you to apply that knowledge to visual problems: why FPN matters for multi-scale detection, how NMS affects mAP, what happens when the camera changes. CV interviews also test domain knowledge that general DL interviews skip — camera calibration, annotation pipeline design, and domain-specific metrics like COCO mAP or Dice coefficient. Preparation should be roughly 60% vision-specific and 40% general deep learning fundamentals.
Upload your resume and the job description. Our AI generates targeted questions based on the specific role — covering detection architectures, segmentation, evaluation metrics, edge deployment, and domain-specific scenarios. Practice with timed responses, camera on, and detailed scoring on both technical accuracy and explanation clarity.
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