Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.
EasyBehavioral
0 practiced
Describe a time you used a cloud-managed ML service such as AWS SageMaker, GCP Vertex AI, or Azure ML. Walk through the end-to-end steps you performed on the platform (dataset upload, training, tuning, deployment), key configuration choices (instance types, IAM roles, VPC), and one operational issue you encountered and how you resolved it.
HardSystem Design
0 practiced
Design an MLOps strategy for training and deploying a 100B-parameter transformer using a hybrid of on-prem GPU racks and cloud spot instances. Cover code distribution, checkpointing strategy (sharded checkpoints vs monolithic), mixed precision, ZeRO or optimizer sharding, fault tolerance for preemptions, and CI practices for large-model changes.
HardTechnical
0 practiced
You are asked to halve the inference latency of a production transformer model running on CPU without changing the model architecture. Propose a detailed optimization plan including runtime selection (ONNX Runtime, OpenVINO, TVM), operator fusion, quantization strategy, kernel tuning, thread affinity, and any code-level inference changes to achieve this goal.
MediumTechnical
0 practiced
Describe how you would use ONNX Runtime for inference in Python. Mention the APIs and session options you would set to optimize performance (for example: InferenceSession, providers such as 'CUDAExecutionProvider', intra_op_num_threads, inter_op_num_threads), and how to measure throughput and latency for CPU and GPU backends.
HardTechnical
0 practiced
Propose a continuous model evaluation strategy in production that monitors fairness, bias and performance on incoming traffic. Include sampling strategies, privacy-preserving metrics, alerting thresholds, remediation workflows (retraining, rollback, human review), and how to gate releases based on these evaluations.
Unlock Full Question Bank
Get access to hundreds of Technical Tools and Stack Proficiency interview questions and detailed answers.
