Easy interview questions in AI & ML, page 6
Closed API vs open-weight models for production
APIs offer top quality and zero ops but recurring per-token cost and data-sharing concerns, open weights give control, privacy, and tuning at the price of hosting and MLOps burden.
Leveraging unlabeled data with limited labels
Pretrain a representation on the million unlabeled images via self-supervision, then fine-tune on the 1,000 labels; or use pseudo-labeling and consistency regularization.
How do you give read-only access to a shared cloud storage bucket?
Bind an IAM role with read permissions to the team at the bucket level, avoid object-level ACLs, and mount read-only on training VMs.
Hugging Face Hub, transformers, and datasets
The Hub hosts models and data, transformers loads models and tokenizers and provides the Trainer, datasets streams and maps preprocessing.
Why was this customer denied: global or local explanation?
This tests matching questions to explanation scope. Global methods show overall behavior; local methods explain one prediction. Specific denials need local methods like SHAP. A red flag is using global summaries like permutation importance or PDPs for a case.
Spark transformations versus actions
Transformations like map and filter are lazy and build a lineage DAG returning a new RDD; actions like count or collect trigger execution and return a value to the driver.
HDFS purpose and fault tolerance
HDFS stores huge files across many commodity machines as large blocks, replicating each block across nodes for fault tolerance; unlike NTFS or ext4 it is distributed, write-once, and optimized for…
The MapReduce paradigm explained
Map applies a function to each input record emitting key-value pairs in parallel; a shuffle groups values by key; reduce aggregates each key's values into a result.
How do you build an HDR image from bracketed exposures?
Align frames, recover the camera response function, merge to a linear radiance map weighted by exposure, then tone map for display.
Outline the classic image stitching pipeline.
Detect and match features like SIFT, estimate a homography with RANSAC, warp and blend with multiband or feathering.
How do you attribute cloud costs to ML projects and implement tagging?
Tests knowledge of resource tagging for cost attribution. A strong answer names provider-specific tags or labels, embeds them in infrastructure-as-code, and activates cost allocation reports.
Differences between on-demand, reserved, and spot EC2 instances?
Tests cost-reliability-commitment tradeoffs for ML infrastructure. Good answers map on-demand to experiments, reserved for production training, and spot to fault-tolerant batch jobs. Red flag: spot for real-time serving or skipping reserved capacity analysis.
Visualizing long-term trend versus seasonality
A line chart over the full three years, often with a moving average, shows the long-term trend; a seasonal plot overlaying each year by month, or a month-of-year box plot, reveals…
Describe a basic lifecycle policy to manage cloud storage costs
This tests cost optimization via tiered storage and automated expiration. Strong answers list transitions from Standard to IA to Glacier, then deletion after set days, plus retrieval costs. A red flag is using manual scripts instead of native lifecycle rules.
What data augmentations help small image datasets?
Apply label-preserving transforms like flips, crops, rotation, color jitter, and mixing to enlarge effective data and reduce overfitting.
Deploy a saved model as a REST prediction service
Load the artifact, wrap it in a predict API, containerize, host with autoscaling, add monitoring.
Precision vs recall in object detection.
Precision is fraction of detections that are correct, recall is fraction of true objects found; prioritize recall for safety-critical detection, precision when false alarms are costly.
Batch prediction versus online real-time prediction
Batch is scheduled bulk scoring, online is low-latency per-request scoring; contrast latency, freshness, cost; give a use case each.
Programmatically detect and redact PII in text
Regex for structured PII plus NER for names and places, redact or tokenize, then validate recall.
Quantifying performance disparity across subgroups
Compute per-group precision, recall, FPR, FNR, compare via fairness metrics; visualize with grouped bars or per-group confusion matrices.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles