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      SuperDuperDB

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      Senior Python Developer Interview

      14 Apr 2023
      Anonymous interview candidate
      No offer
      Negative experience
      Easy interview

      Application

      I applied online. The process took 2 weeks. I interviewed at SuperDuperDB in Mar 2023

      Interview

      They sent me a take home assignment: "Your task is to write a python function which accepts a dictionary as input and returns a dictionary as output. The output dictionary should be a modified version of the input dictionary. For every key-value pair in the input, then, if the value is not a dictionary, you should replace the value (`old_value`) with `{'_content': old_value, '_type': str(type(old_value))}`. If the value is a dictionary, then you perform the same substitution on that value, etc. The function should optionally also accept a parameter specifiying to which depth this substitution should proceed (how many iterations into the sub-dictionaries etc.). Please write the python function in a single standalone script `substitute.py`. Your answer should run in any `python3` environment, and should not require any package imports. The script should accept 3 command line parameters - the file-name of a JSON file which is loaded and passed to the first parameter of the function, and an integer which is passed to the second parameter of the function, specifying the depth of the substitution, and finally the path of the output file. The script should save the output of the function to the output file. For submissing what you have done: Please create a private project on Github with the following naming: `SuperDuperDB_coding_task_python_YOURNAME` and invite <employees> once you are done. Also please ping us here. Thank you!" After I completed that they sent me a questionnaire asking for links such as github and linkedin, salary expectations, and a list of what technologies I've used. I got rejected here, although there was no reason for them to ask this AFTER I did the take home assignment.

      Interview questions [1]

      Question 1

      Which cloud providers have you worked with in a professional context? (AWS, GCloud, Azure, Other) Which deep-learning/ ML frameworks have you worked with? (PyTorch, Tensorflow, Sklearn, XGBoost, Jax, Other) Which automation/ CI-CD tools are you familiar with (GitHub actions, Ansible, Apache Airflow, Travis, CircleCI, Other) Which serving frameworks are you familiar with? (Flask, Nginx, GRPC, Other) Which orchestration and containerization frameworks are you familiar with? (Docker, Docker compose, Kubernetes, ECS, Fargate, EKS, Other) Which databases/ -warehouses/ -lakes have you worked with in the past? (MongoDB, SQL, PostGreSQL, Snowflake, Databricks, Redis, Google Big Query, ElasticSearch, Other) Which MLOps tooling are you familiar with? (DVC, TorchServe, AWS Sagemaker, Weights&Biases, Feast KubeFlow, MLFlow, Prefect, TensorFlow Extended, (TFX) Serving, Other) Which of the following scientific libraries have you worked with? (numpy, scipy, matplotlib, pandas, numba, dask, Other) Which of the following libraries for high performance computing to you have experience with? (CUDA, Deepspeed, Horovod, NCCL, MPI, Other) Which of the following deep learning techniques could you explain? (Batch normalization, Gradient checkpointing, Quantization, Mixed precision training, Other) Which of the following libraries or databases for vector search have you played with? (Faiss, Milvus Pinecone, ScaNN, Qdrant, Chroma, ElasticSearch similarity search, Other)
      Answer question
      2

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