Covers how candidates proactively maintain and expand their technical skills while monitoring and evaluating broader technology trends relevant to their domain. Candidates should be able to describe information sources such as academic papers, preprint servers, standards bodies, security advisories, vendor release notes, conferences, workshops, training courses, certifications, open source communities, and professional mailing lists. They should explain hands on strategies including building proof of concept systems, sandbox testing, lab experiments, prototypes, pilot projects, and tool evaluations, and how they assess trade offs such as security and privacy implications, compatibility, maintainability, performance, cost, and operational complexity before adoption. Interviewers may probe how the candidate distinguishes hype from durable improvements, measures the impact of new technologies on product quality and delivery, introduces and pilots changes within a team, balances short term delivery with long term technical investment, and decides when to deprecate older practices. The topic also includes practices for sharing knowledge through documentation, internal training, mentorship, and open source contributions.
MediumTechnical
0 practiced
Outline experiments to evaluate a model compression technique such as pruning, quantization, or distillation. For each technique, define how you will measure accuracy drop, latency improvement, memory reduction, throughput gain, and energy usage. Explain how you will determine statistical significance and practical acceptance thresholds for production use.
EasyTechnical
0 practiced
What are model cards and dataset datasheets? Explain their purpose, typical contents (intended use, limitations, metrics, training data provenance, ethical considerations), and how you would integrate them into your team's ML lifecycle so they are created and reviewed before production deployment.
MediumTechnical
0 practiced
List the steps you would take to evaluate the security and privacy implications of adopting a new ML library or a third-party pre-trained model. Include threat modeling, dependency scanning, data exposure analysis, license review, compliance checks, and a remediation/mitigation plan for discovered risks.
MediumTechnical
0 practiced
Estimate compute and monetary cost to train a transformer-scale model (~1B parameters) on your dataset and describe strategies to reduce cost: mixed precision, gradient accumulation, transfer learning using pre-trained checkpoints, distillation, dataset pruning/sampling, and using spot instances. Provide rough quantitative reasoning about how each technique affects FLOPs, memory, and cost.
MediumSystem Design
0 practiced
Describe how you would create an isolated sandbox environment for testing new ML libraries and model versions. Include environment provisioning (containers, virtualenvs, Kubernetes namespaces), data access controls (anonymized or synthetic data), dependency pinning and SBOMs, and steps to ensure meaningful parity with production for performance and behavior.
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