Interview questions in AI & ML, page 8
Describe a basic RAG architecture and its two main components
This tests retrieval-generation separation. Good answers name the retriever, which fetches relevant documents, and the generator, which synthesizes an answer using those documents plus the query.
Design an incremental load pipeline from a transactional DB to a warehouse
Contrast timestamp watermarking, CDC from transaction logs, and open-table incremental reads; cite merge logic and idempotency.
Regularization techniques for an overfitting CNN
Data augmentation expands the effective dataset, dropout prevents co-adaptation, weight decay penalizes large weights, plus early stopping and batchnorm.
How do you version and distribute Docker dev environments consistently?
Tests immutable dev environment distribution. Strong answers cover: versioned Dockerfiles in Git, immutable image tags pushed to a registry, and enforcing identical pulls for CI and developers. Red flag: using the "latest" tag or local Dockerfile rebuilds.
Why does your RAG ignore or contradict retrieved context?
Tests separation of retrieval failures from generation grounding in RAG. Strong answers trace symptoms to root causes like bad chunks, prompt ordering, or parametric knowledge override, then outline systematic debugging. Do not just say hallucination.
How would you evade an advanced anti-bot system while scraping?
Discuss JA3/TLS spoofing, CDP-based browser automation, human-like mouse paths and delays, and session consistency.
Evaluating a RAG system end to end
Measure retrieval with context recall or precision, and generation with faithfulness and answer relevance, attributing failures to the right stage.
ResNet residual blocks and the degradation problem
A residual block learns F(x) and adds the identity input x, so layers fit a residual; this eases gradient flow and solves the degradation problem.
Describe two secure methods for providing secrets to a running container
Mention runtime mounts like Docker secrets, orchestrator secret injection, and cloud IAM patterns.
Design a scalable, fault-tolerant real-time IoT data ingestion system
This tests separation of edge connectivity, buffering, and processing. A strong answer names an edge gateway, Kafka as the backplane, stream processing, and cold storage, plus backpressure and partitioning.
Handling missing numerical values
Dropping rows is simple but loses data and can bias if missingness is non-random; mean or median imputation keeps rows but shrinks variance and ignores correlations; model-based imputation is…
Receptive fields in convolutional networks
Receptive field is the input region affecting a neuron; it grows with depth, larger kernels, and stride. It matters for capturing context in detection and segmentation.
Strategies to reduce a 5GB ML Docker image size
Tests multi-stage build hygiene and ML bloat reduction. Strong answers use multi-stage builds, strip CUDA dev libs, use slim bases, and collapse cache cleanup into one RUN. Red flag: rm -rf in a separate RUN step, which still bloats the layer.
How would you modify retrieval architecture for hybrid text and SQL RAG?
It tests unified retrieval across unstructured text and structured SQL. Outline a query planner that routes to vector search or text-to-SQL, joins the results, and synthesizes a final answer. Never suggest embedding the whole database as text chunks.
Hybrid search and re-ranking for retrieval
Hybrid search fuses dense semantic and sparse keyword signals to catch exact terms dense misses; a cross-encoder re-ranker rescoring top-k boosts precision.
Uses of the 1x1 convolution
A 1x1 conv is a per-pixel linear combination across channels; it reshapes channel depth cheaply and adds nonlinearity. Uses: dimensionality reduction in bottlenecks and channel mixing.
Design on-demand containerized dev environments for data scientists
Tests multi-tenant notebook infrastructure design. Cover a Notebook Controller, curated Jupyter and VS Code images, namespace isolation with RBAC, resource quotas, and persistent storage. Red flag: a single shared VM without tenancy or idle shutdown.
Adapting a classification CNN for segmentation
Replace the dense head with conv layers, upsample via transposed convolutions, and fuse encoder skip connections to recover spatial detail lost to downsampling.
How do you containerize a Python training script for GPU cloud VMs?
This tests reproducible GPU containerization. A strong answer uses an NVIDIA CUDA base image, installs Python dependencies at build time, copies the training script, and runs with --gpus.
Identify RAG latency bottlenecks and propose optimizations
This tests systems thinking across the RAG pipeline. A strong answer names four bottlenecks—embedding, search, chunking, and generation—and pairs each with caching, index tuning, and distillation. Red flag: GPU scaling without indexing fixes.
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