Relevant Technical Experience and Projects Questions
Describe the hands on technical work and projects that directly relate to the role. Cover specific tools and platforms you used, such as forensic analysis tools, operating systems, networking and mobile analysis utilities, analytics and database tools, and embedded systems or microcontroller development work. For each item explain your role, the scope and scale of the work, key technical decisions, measurable outcomes or improvements, and what you learned. Include relevant certifications and training when they reinforced your technical skills. Also discuss any process improvements you drove, cross functional collaboration required, and how the project experience demonstrates readiness for the role.
MediumBehavioral
0 practiced
Tell me about a project where you introduced MLflow or Weights & Biases to improve experiment reproducibility and operational workflows. Explain how you integrated the tool into training scripts, configured artifact backends (S3 or GCS), implemented access control and model promotion policies, and provide measurable outcomes such as reduced experiment debugging time, faster model promotion cycles, or fewer production incidents.
MediumTechnical
0 practiced
You are building a classifier where the positive class prevalence is 0.5 percent. Describe your end-to-end approach: preprocessing and sampling strategies (undersampling, oversampling, SMOTE), model and loss choices (class weights, focal loss), evaluation metrics and validation strategy (precision@k, AUCPR, stratified cross-validation), and tooling you would use (scikit-learn, imbalanced-learn). Also explain how you would simulate realistic production imbalance during validation and guard against label leakage.
MediumSystem Design
0 practiced
Design an architecture to serve nearest-neighbor recommendations over 50 million item embeddings at 1000 queries per second with a 50ms tail latency SLO. Compare using FAISS, Milvus, or a managed vector DB like Pinecone in terms of index types (IVF, HNSW), memory versus disk trade-offs (PQ, OPQ), GPU versus CPU indexing, sharding, caching hot items, and operational complexity. Explain how you would measure precision/recall versus latency and plan for index rebuilds and online updates.
MediumSystem Design
0 practiced
Design a CI/CD pipeline for machine learning models that supports development, staging, and production environments. The pipeline must run unit tests and linting, execute data validation, perform smoke training or training orchestration, register model artifacts in a model registry, build and push container images to a registry, deploy a canary to Kubernetes, run automated acceptance tests against the canary, and promote to production if tests pass. Include recommended tooling choices (GitHub Actions or Azure Pipelines, MLflow, Docker, Helm, ArgoCD), security scanning and signing, and a rollback strategy that ensures safe promotions for long-running training steps.
MediumTechnical
0 practiced
Using GCP (BigQuery + Vertex AI) or AWS (S3 + SageMaker) as your cloud stack, describe an end-to-end pattern for production training and serving of large tabular models. Discuss data storage choices, ETL vs ELT tradeoffs, feature materialization, choices for spot or preemptible instances for training, model registry, online serving strategies for low latency, and considerations around egress costs and data locality.
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